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  • 1.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. FOI, SE-58111 Linkoping, Sweden.
    Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery2017In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 55, no 2, p. 658-670Article in journal (Refereed)
    Abstract [en]

    We address the problem of estimating atmosphere parameters (temperature and water vapor content) from data captured by an airborne thermal hyperspectral imager and propose a method based on linear and nonlinear optimization. The method is used for the estimation of the parameters (temperature and emissivity) of the observed object as well as sensor gain under certain restrictions. The method is analyzed with respect to sensitivity to noise and the number of spectral bands. Simulations with synthetic signatures are performed to validate the analysis, showing that the estimation can be performed with as few as 10-20 spectral bands at moderate noise levels. The proposed method is also extended to exploit additional knowledge, for example, measurements of atmospheric parameters and sensor noise. Additionally, we show how to extend the method in order to improve spectral calibration.

  • 2.
    Ahlberg, Jörgen
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Arsic, Dejan
    Munich University of Technology, Germany.
    Ganchev, Todor
    University of Patras, Greece.
    Linderhed, Anna
    FOI Swedish Defence Research Agency.
    Menezes, Paolo
    University of Coimbra, Portugal.
    Ntalampiras, Stavros
    University of Patras, Greece.
    Olma, Tadeusz
    MARAC S.A., Greece.
    Potamitis, Ilyas
    Technological Educational Institute of Crete, Greece.
    Ros, Julien
    Probayes SAS, France.
    Prometheus: Prediction and interpretation of human behaviour based on probabilistic structures and heterogeneous sensors2008Conference paper (Refereed)
    Abstract [en]

    The on-going EU funded project Prometheus (FP7-214901) aims at establishing a general framework which links fundamental sensing tasks to automated cognition processes enabling interpretation and short-term prediction of individual and collective human behaviours in unrestricted environments as well as complex human interactions. To achieve the aforementioned goals, the Prometheus consortium works on the following core scientific and technological objectives:

    1. sensor modeling and information fusion from multiple, heterogeneous perceptual modalities;

    2. modeling, localization, and tracking of multiple people;

    3. modeling, recognition, and short-term prediction of continuous complex human behavior.

  • 3.
    Ahlberg, Jörgen
    et al.
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Berg, Amanda
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Evaluating Template Rescaling in Short-Term Single-Object Tracking2015Conference paper (Refereed)
    Abstract [en]

    In recent years, short-term single-object tracking has emerged has a popular research topic, as it constitutes the core of more general tracking systems. Many such tracking methods are based on matching a part of the image with a template that is learnt online and represented by, for example, a correlation filter or a distribution field. In order for such a tracker to be able to not only find the position, but also the scale, of the tracked object in the next frame, some kind of scale estimation step is needed. This step is sometimes separate from the position estimation step, but is nevertheless jointly evaluated in de facto benchmarks. However, for practical as well as scientific reasons, the scale estimation step should be evaluated separately – for example,theremightincertainsituationsbeothermethodsmore suitable for the task. In this paper, we describe an evaluation method for scale estimation in template-based short-term single-object tracking, and evaluate two state-of-the-art tracking methods where estimation of scale and position are separable.

  • 4.
    Ahlberg, Jörgen
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Glana Sensors AB, Sweden.
    Renhorn, Ingmar
    Glana Sensors AB, Sweden.
    Chevalier, Tomas
    Scienvisic AB, Sweden.
    Rydell, Joakim
    FOI, Swedish Defence Research Agency, Sweden.
    Bergström, David
    FOI, Swedish Defence Research Agency, Sweden.
    Three-dimensional hyperspectral imaging technique2017In: ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XXIII / [ed] Miguel Velez-Reyes; David W. Messinger, SPIE - International Society for Optical Engineering, 2017, Vol. 10198, article id 1019805Conference paper (Refereed)
    Abstract [en]

    Hyperspectral remote sensing based on unmanned airborne vehicles is a field increasing in importance. The combined functionality of simultaneous hyperspectral and geometric modeling is less developed. A configuration has been developed that enables the reconstruction of the hyperspectral three-dimensional (3D) environment. The hyperspectral camera is based on a linear variable filter and a high frame rate, high resolution camera enabling point-to-point matching and 3D reconstruction. This allows the information to be combined into a single and complete 3D hyperspectral model. In this paper, we describe the camera and illustrate capabilities and difficulties through real-world experiments.

  • 5.
    Ahlberg, Jörgen
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Åstrom, Anders
    Swedish Natl Forens Ctr NFC, Linkoping, Sweden.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
    Simultaneous sensing, readout, and classification on an intensity-ranking image sensor2018In: International journal of circuit theory and applications, ISSN 0098-9886, E-ISSN 1097-007X, Vol. 46, no 9, p. 1606-1619Article in journal (Refereed)
    Abstract [en]

    We combine the near-sensor image processing concept with address-event representation leading to an intensity-ranking image sensor (IRIS) and show the benefits of using this type of sensor for image classification. The functionality of IRIS is to output pixel coordinates (X and Y values) continuously as each pixel has collected a certain number of photons. Thus, the pixel outputs will be automatically intensity ranked. By keeping track of the timing of these events, it is possible to record the full dynamic range of the image. However, in many cases, this is not necessary-the intensity ranking in itself gives the needed information for the task at hand. This paper describes techniques for classification and proposes a particular variant (groves) that fits the IRIS architecture well as it can work on the intensity rankings only. Simulation results using the CIFAR-10 dataset compare the results of the proposed method with the more conventional ferns technique. It is concluded that the simultaneous sensing and classification obtainable with the IRIS sensor yields both fast (shorter than full exposure time) and processing-efficient classification.

  • 6.
    Ahlman, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Improved Temporal Resolution Using Parallel Imaging in Radial-Cartesian 3D functional MRI2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    MRI (Magnetic Resonance Imaging) is a medical imaging method that uses magnetic fields in order to retrieve images of the human body. This thesis revolves around a novel acquisition method of 3D fMRI (functional Magnetic Resonance Imaging) called PRESTO-CAN that uses a radial pattern in order to sample the (kx,kz)-plane of k-space (the frequency domain), and a Cartesian sample pattern in the ky-direction. The radial sample pattern allows for a denser sampling of the central parts of k-space, which contain the most basic frequency information about the structure of the recorded object. This allows for higher temporal resolution to be achieved compared with other sampling methods since a fewer amount of total samples are needed in order to retrieve enough information about how the object has changed over time. Since fMRI is mainly used for monitoring blood flow in the brain, increased temporal resolution means that we can be able to track fast changes in brain activity more efficiently.The temporal resolution can be further improved by reducing the time needed for scanning, which in turn can be achieved by applying parallel imaging. One such parallel imaging method is SENSE (SENSitivity Encoding). The scan time is reduced by decreasing the sampling density, which causes aliasing in the recorded images. The aliasing is removed by the SENSE method by utilizing the extra information provided by the fact that multiple receiver coils with differing sensitivities are used during the acquisition. By measuring the sensitivities of the respective receiver coils and solving an equation system with the aliased images, it is possible to calculate how they would have looked like without aliasing.In this master thesis, SENSE has been successfully implemented in PRESTO-CAN. By using normalized convolution in order to refine the sensitivity maps of the receiver coils, images with satisfying quality was able to be reconstructed when reducing the k-space sample rate by a factor of 2, and images of relatively good quality also when the sample rate was reduced by a factor of 4. In this way, this thesis has been able to contribute to the improvement of the temporal resolution of the PRESTO-CAN method.

  • 7.
    Ahlman, Gustav
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Magnusson, Maria
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Increased temporal resolution in radial-Cartesian sampling of k-space by implementation of parallel imaging2011Conference paper (Refereed)
  • 8.
    Andersson, Elin
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Thermal Impact of a Calibrated Stereo Camera Rig2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Measurements performed from stereo reconstruction can be obtained with a high accuracy with correct calibrated cameras. A stereo camera rig mounted in an outdoor environment is exposed to temperature changes, which has an impact of the calibration of the cameras.

    The aim of the master thesis was to investigate the thermal impact of a calibrated stereo camera rig. This was performed by placing a stereo rig in a temperature chamber and collect data of a calibration board at different temperatures. Data was collected with two different cameras and lensesand used for calibration of the stereo camera rig for different scenarios. The obtained parameters were plotted and analyzed.

    The result from the master thesis gives that the thermal variation has an impact of the accuracy of the calibrated stereo camera rig. A calibration obtained in one temperature can not be used for a different temperature without a degradation of the accuracy. The plotted parameters from the calibration had a high noise level due to problems with the calibration methods, and no visible trend from temperature changes could be seen.

  • 9.
    Andersson, Maria
    et al.
    FOI Swedish Defence Research Agency.
    Rydell, Joakim
    FOI Swedish Defence Research Agency.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. FOI Swedish Defence Research Agency.
    Estimation of crowd behaviour using sensor networks and sensor fusion2009Conference paper (Refereed)
    Abstract [en]

    Commonly, surveillance operators are today monitoring a large number of CCTV screens, trying to solve the complex cognitive tasks of analyzing crowd behavior and detecting threats and other abnormal behavior. Information overload is a rule rather than an exception. Moreover, CCTV footage lacks important indicators revealing certain threats, and can also in other respects be complemented by data from other sensors. This article presents an approach to automatically interpret sensor data and estimate behaviors of groups of people in order to provide the operator with relevant warnings. We use data from distributed heterogeneous sensors (visual cameras and a thermal infrared camera), and process the sensor data using detection algorithms. The extracted features are fed into a hidden Markov model in order to model normal behavior and detect deviations. We also discuss the use of radars for weapon detection.

  • 10.
    Andersson, Viktor
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Semantic Segmentation: Using Convolutional Neural Networks and Sparse dictionaries2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance?

    The dataset used here is the Cityscapes-dataset which is a library of 25000 labeled road scene images.The sparse dictionary was acquired using the K-SVD method. The filters were added to two different networks whose performance was tested individually. One of the architectures is much deeper than the other. The results have been presented for both networks. The results show that filter initialization is an important aspect which should be taken into consideration while training the deep networks for semantic segmentation.

  • 11.
    Anwer, Rao Muhammad
    et al.
    Aalto Univ, Finland.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Laaksonen, Jorma
    Aalto Univ, Finland.
    Two-Stream Part-based Deep Representation for Human Attribute Recognition2018In: 2018 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB), IEEE , 2018, p. 90-97Conference paper (Refereed)
    Abstract [en]

    Recognizing human attributes in unconstrained environments is a challenging computer vision problem. State-of-the-art approaches to human attribute recognition are based on convolutional neural networks (CNNs). The de facto practice when training these CNNs on a large labeled image dataset is to take RGB pixel values of an image as input to the network. In this work, we propose a two-stream part-based deep representation for human attribute classification. Besides the standard RGB stream, we train a deep network by using mapped coded images with explicit texture information, that complements the standard RGB deep model. To integrate human body parts knowledge, we employ the deformable part-based models together with our two-stream deep model. Experiments are performed on the challenging Human Attributes (HAT-27) Dataset consisting of 27 different human attributes. Our results clearly show that (a) the two-stream deep network provides consistent gain in performance over the standard RGB model and (b) that the attribute classification results are further improved with our two-stream part-based deep representations, leading to state-of-the-art results.

  • 12.
    Anwer, Rao Muhammad
    et al.
    Aalto Univ, Finland.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    van de Weijer, Joost
    Univ Autonoma Barcelona, Spain.
    Molinier, Matthieu
    VTT Tech Res Ctr Finland Ltd, Finland.
    Laaksonen, Jorma
    Aalto Univ, Finland.
    Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification2018In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 138, p. 74-85Article in journal (Refereed)
    Abstract [en]

    Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

  • 13.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Larsson, Fredrik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas B.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Bicycle Tracking Using Ellipse Extraction2011In: Proceedings of the 14thInternational Conference on Information Fusion, 2011, IEEE , 2011, p. 1-8Conference paper (Refereed)
    Abstract [en]

    A new approach to track bicycles from imagery sensor data is proposed. It is based on detecting ellipsoids in the images, and treat these pair-wise using a dynamic bicycle model. One important application area is in automotive collision avoidance systems, where no dedicated systems for bicyclists yet exist and where very few theoretical studies have been published.

    Possible conflicts can be predicted from the position and velocity state in the model, but also from the steering wheel articulation and roll angle that indicate yaw changes before the velocity vector changes. An algorithm is proposed which consists of an ellipsoid detection and estimation algorithm and a particle filter.

    A simulation study of three critical single target scenarios is presented, and the algorithm is shown to produce excellent state estimates. An experiment using a stationary camera and the particle filter for state estimation is performed and has shown encouraging results.

  • 14.
    Baravdish, George
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Svensson, Olof
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Åström, Freddie
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    On Backward p(x)-Parabolic Equations for Image Enhancement2015In: Numerical Functional Analysis and Optimization, ISSN 0163-0563, E-ISSN 1532-2467, Vol. 36, no 2, p. 147-168Article in journal (Refereed)
    Abstract [en]

    In this study, we investigate the backward p(x)-parabolic equation as a new methodology to enhance images. We propose a novel iterative regularization procedure for the backward p(x)-parabolic equation based on the nonlinear Landweber method for inverse problems. The proposed scheme can also be extended to the family of iterative regularization methods involving the nonlinear Landweber method. We also investigate the connection between the variable exponent p(x) in the proposed energy functional and the diffusivity function in the corresponding Euler-Lagrange equation. It is well known that the forward problems converges to a constant solution destroying the image. The purpose of the approach of the backward problems is twofold. First, solving the backward problem by a sequence of forward problems we obtain a smooth image which is denoised. Second, by choosing the initial data properly we try to reduce the blurriness of the image. The numerical results for denoising appear to give improvement over standard methods as shown by preliminary results.

  • 15.
    Barnada, Marc
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe University of Frankfurt, Germany.
    Conrad, Christian
    Goethe University of Frankfurt, Germany.
    Bradler, Henry
    Goethe University of Frankfurt, Germany.
    Ochs, Matthias
    Goethe University of Frankfurt, Germany.
    Mester, Rudolf
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe University of Frankfurt, Germany.
    Estimation of Automotive Pitch, Yaw, and Roll using Enhanced Phase Correlation on Multiple Far-field Windows2015In: 2015 IEEE Intelligent Vehicles Symposium (IV), IEEE , 2015, p. 481-486Conference paper (Refereed)
    Abstract [en]

    The online-estimation of yaw, pitch, and roll of a moving vehicle is an important ingredient for systems which estimate egomotion, and 3D structure of the environment in a moving vehicle from video information. We present an approach to estimate these angular changes from monocular visual data, based on the fact that the motion of far distant points is not dependent on translation, but only on the current rotation of the camera. The presented approach does not require features (corners, edges,...) to be extracted. It allows to estimate in parallel also the illumination changes from frame to frame, and thus allows to largely stabilize the estimation of image correspondences and motion vectors, which are most often central entities needed for computating scene structure, distances, etc. The method is significantly less complex and much faster than a full egomotion computation from features, such as PTAM [6], but it can be used for providing motion priors and reduce search spaces for more complex methods which perform a complete analysis of egomotion and dynamic 3D structure of the scene in which a vehicle moves.

  • 16.
    Bengtsson, Morgan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Indoor 3D Mapping using Kinect2014Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In recent years several depth cameras have emerged on the consumer market, creating many interesting possibilities forboth professional and recreational usage. One example of such a camera is the Microsoft Kinect sensor originally usedwith the Microsoft Xbox 360 game console. In this master thesis a system is presented that utilizes this device in order to create an as accurate as possible 3D reconstruction of an indoor environment. The major novelty of the presented system is the data structure based on signed distance fields and voxel octrees used to represent the observed environment.

  • 17.
    Berg, Amanda
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Classification of leakage detections acquired by airborne thermography of district heating networks2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In Sweden and many other northern countries, it is common for heat to be distributed to homes and industries through district heating networks. Such networks consist of pipes buried underground carrying hot water or steam with temperatures in the range of 90-150 C. Due to bad insulation or cracks, heat or water leakages might appear.

    A system for large-scale monitoring of district heating networks through remote thermography has been developed and is in use at the company Termisk Systemteknik AB. Infrared images are captured from an aircraft and analysed, finding and indicating the areas for which the ground temperature is higher than normal. During the analysis there are, however, many other warm areas than true water or energy leakages that are marked as detections. Objects or phenomena that can cause false alarms are those who, for some reason, are warmer than their surroundings, for example, chimneys, cars and heat leakages from buildings.

    During the last couple of years, the system has been used in a number of cities. Therefore, there exists a fair amount of examples of different types of detections. The purpose of the present master’s thesis is to evaluate the reduction of false alarms of the existing analysis that can be achieved with the use of a learning system, i.e. a system which can learn how to recognize different types of detections. 

    A labelled data set for training and testing was acquired by contact with customers. Furthermore, a number of features describing the intensity difference within the detection, its shape and propagation as well as proximity information were found, implemented and evaluated. Finally, four different classifiers and other methods for classification were evaluated.

    The method that obtained the best results consists of two steps. In the initial step, all detections which lie on top of a building are removed from the data set of labelled detections. The second step consists of classification using a Random forest classifier. Using this two-step method, the number of false alarms is reduced by 43% while the percentage of water and energy detections correctly classified is 99%.

  • 18.
    Berg, Amanda
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Detection and Tracking in Thermal Infrared Imagery2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as it is possible to measure a temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy.

    This thesis addresses the problem of detection and tracking in thermal infrared imagery. Visual detection and tracking of objects in video are research areas that have been and currently are subject to extensive research. Indications oftheir popularity are recent benchmarks such as the annual Visual Object Tracking (VOT) challenges, the Object Tracking Benchmarks, the series of workshops on Performance Evaluation of Tracking and Surveillance (PETS), and the workshops on Change Detection. Benchmark results indicate that detection and tracking are still challenging problems.

    A common belief is that detection and tracking in thermal infrared imagery is identical to detection and tracking in grayscale visual imagery. This thesis argues that the preceding allegation is not true. The characteristics of thermal infrared radiation and imagery pose certain challenges to image analysis algorithms. The thesis describes these characteristics and challenges as well as presents evaluation results confirming the hypothesis.

    Detection and tracking are often treated as two separate problems. However, some tracking methods, e.g. template-based tracking methods, base their tracking on repeated specific detections. They learn a model of the object that is adaptively updated. That is, detection and tracking are performed jointly. The thesis includes a template-based tracking method designed specifically for thermal infrared imagery, describes a thermal infrared dataset for evaluation of template-based tracking methods, and provides an overview of the first challenge on short-term,single-object tracking in thermal infrared video. Finally, two applications employing detection and tracking methods are presented.

    List of papers
    1. A Thermal Object Tracking Benchmark
    Open this publication in new window or tab >>A Thermal Object Tracking Benchmark
    2015 (English)Conference paper, Published paper (Refereed)
    Abstract [en]

    Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. In order to evaluate STSO tracking algorithms on visual imagery, there are de facto standard benchmarks. However, we argue that tracking in thermal imagery is different than in visual imagery, and that a separate benchmark is needed. The available thermal infrared datasets are few and the existing ones are not challenging for modern tracking algorithms. Therefore, we hereby propose a thermal infrared benchmark according to the Visual Object Tracking (VOT) protocol for evaluation of STSO tracking methods. The benchmark includes the new LTIR dataset containing 20 thermal image sequences which have been collected from multiple sources and annotated in the format used in the VOT Challenge. In addition, we show that the ranking of different tracking principles differ between the visual and thermal benchmarks, confirming the need for the new benchmark.

    Place, publisher, year, edition, pages
    IEEE, 2015
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-121001 (URN)10.1109/AVSS.2015.7301772 (DOI)000380619700052 ()978-1-4673-7632-7 (ISBN)
    Conference
    12th IEEE International Conference on Advanced Video- and Signal-based Surveillance, Karlsruhe, Germany, August 25-28 2015
    Available from: 2015-09-02 Created: 2015-09-02 Last updated: 2018-01-11Bibliographically approved
    2. The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
    Open this publication in new window or tab >>The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
    Show others...
    2015 (English)In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 639-651Conference paper, Published paper (Refereed)
    Abstract [en]

    The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2015
    Series
    IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-126917 (URN)10.1109/ICCVW.2015.86 (DOI)000380434700077 ()978-146738390-5 (ISBN)
    External cooperation:
    Conference
    IEEE International Conference on Computer Vision Workshop (ICCVW. 7-13 Dec. 2015 Santiago, Chile
    Available from: 2016-04-07 Created: 2016-04-07 Last updated: 2018-01-10Bibliographically approved
    3. Channel Coded Distribution Field Tracking for Thermal Infrared Imagery
    Open this publication in new window or tab >>Channel Coded Distribution Field Tracking for Thermal Infrared Imagery
    2016 (English)In: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), IEEE , 2016, p. 1248-1256Conference paper, Published paper (Refereed)
    Abstract [en]

    We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. The fast progress has been possible thanks to the development of new template-based tracking methods with online template updates, methods which have not been explored for TIR tracking. Instead, tracking methods used for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed. Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a template-based tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. In order to avoid background contamination of the object template, we propose to exploit background information for the online template update and to adaptively select the object region used for tracking. Moreover, we propose a novel method for estimating object scale change. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Further, the proposed tracker, ABCD, and the VOT-TIR2015 winner SRDCFir are evaluated on maritime data. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

    Place, publisher, year, edition, pages
    IEEE, 2016
    Series
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-134402 (URN)10.1109/CVPRW.2016.158 (DOI)000391572100151 ()978-1-5090-1438-5 (ISBN)978-1-5090-1437-8 (ISBN)
    Conference
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on
    Funder
    Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784EU, FP7, Seventh Framework Programme, 607567
    Available from: 2017-02-09 Created: 2017-02-09 Last updated: 2018-01-13
    4. Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera
    Open this publication in new window or tab >>Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera
    2015 (English)In: Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Rasmus R. Paulsen; Kim S. Pedersen, Springer, 2015, p. 492-503Conference paper, Published paper (Refereed)
    Abstract [en]

    We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway. While the problem at a first glance looks similar to road and lane detection, which in the past has been a popular research topic, a closer look reveals that the problem at hand is previously unaddressed. As a consequence, relevant datasets are missing as well, and thus our contribution is two-fold: We propose an approach to the novel problem of obstacle detection on railways and we describe the acquisition of a novel data set.

    Place, publisher, year, edition, pages
    Springer, 2015
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9127
    Keywords
    Thermal imaging; Computer vision; Train safety; Railway detection; Anomaly detection; Obstacle detection
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-119507 (URN)10.1007/978-3-319-19665-7_42 (DOI)978-3-319-19664-0 (ISBN)978-3-319-19665-7 (ISBN)
    Conference
    19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015
    Available from: 2015-06-22 Created: 2015-06-18 Last updated: 2018-02-07Bibliographically approved
    5. Enhanced analysis of thermographic images for monitoring of district heat pipe networks
    Open this publication in new window or tab >>Enhanced analysis of thermographic images for monitoring of district heat pipe networks
    2016 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 83, no 2, p. 215-223Article in journal (Refereed) Published
    Abstract [en]

    We address two problems related to large-scale aerial monitoring of district heating networks. First, we propose a classification scheme to reduce the number of false alarms among automatically detected leakages in district heating networks. The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps; by (a) using a building segmentation scheme in order to remove detections on buildings, and (b) to use a machine learning approach to classify the remaining detections as true or false leakages. We provide extensive experimental analysis on real-world data, showing that this post-processing step significantly improves the usefulness of the system. Second, we propose a method for characterization of leakages over time, i.e., repeating the image acquisition one or a few years later and indicate areas that suffer from an increased energy loss. We address the problem of finding trends in the degradation of pipe networks in order to plan for long-term maintenance, and propose a visualization scheme exploiting the consecutive data collections.

    Place, publisher, year, edition, pages
    Elsevier, 2016
    Keywords
    Remote thermography; Classification; Pattern recognition; District heating; Thermal infrared
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-133004 (URN)10.1016/j.patrec.2016.07.002 (DOI)000386874800013 ()
    Note

    Funding Agencies|Swedish Research Council (Vetenskapsradet) through project Learning systems for remote thermography [621-2013-5703]; Swedish Research Council [2014-6227]

    Available from: 2016-12-08 Created: 2016-12-07 Last updated: 2018-11-26
  • 19.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Classification and temporal analysis of district heating leakages in thermal images2014In: Proceedings of The 14th International Symposium on District Heating and Cooling, 2014Conference paper (Other academic)
    Abstract [en]

    District heating pipes are known to degenerate with time and in some cities the pipes have been used for several decades. Due to bad insulation or cracks, energy or media leakages might appear. This paper presents a complete system for large-scale monitoring of district heating networks, including methods for detection, classification and temporal characterization of (potential) leakages. The system analyses thermal infrared images acquired by an aircraft-mounted camera, detecting the areas for which the pixel intensity is higher than normal. Unfortunately, the system also finds many false detections, i.e., warm areas that are not caused by media or energy leakages. Thus, in order to reduce the number of false detections we describe a machine learning method to classify the detections. The results, based on data from three district heating networks show that we can remove more than half of the false detections. Moreover, we also propose a method to characterize leakages over time, that is, repeating the image acquisition one or a few years later and indicate areas that suffer from an increased energy loss.

  • 20.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Classification of leakage detections acquired by airborne thermography of district heating networks2014In: 2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), IEEE , 2014, p. 1-4Conference paper (Refereed)
    Abstract [en]

    We address the problem of reducing the number offalse alarms among automatically detected leakages in districtheating networks. The leakages are detected in images capturedby an airborne thermal camera, and each detection correspondsto an image region with abnormally high temperature. Thisapproach yields a significant number of false positives, and wepropose to reduce this number in two steps. First, we use abuilding segmentation scheme in order to remove detectionson buildings. Second, we extract features from the detectionsand use a Random forest classifier on the remaining detections.We provide extensive experimental analysis on real-world data,showing that this post-processing step significantly improves theusefulness of the system.

  • 21.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB Linköping, Sweden.
    Classifying district heating network leakages in aerial thermal imagery2014Conference paper (Other academic)
    Abstract [en]

    In this paper we address the problem of automatically detecting leakages in underground pipes of district heating networks from images captured by an airborne thermal camera. The basic idea is to classify each relevant image region as a leakage if its temperature exceeds a threshold. This simple approach yields a significant number of false positives. We propose to address this issue by machine learning techniques and provide extensive experimental analysis on real-world data. The results show that this postprocessing step significantly improves the usefulness of the system.

  • 22.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    A thermal infrared dataset for evaluation of short-term tracking methods2015Conference paper (Other academic)
    Abstract [en]

    During recent years, thermal cameras have decreased in both size and cost while improving image quality. The area of use for such cameras has expanded with many exciting applications, many of which require tracking of objects. While being subject to extensive research in the visual domain, tracking in thermal imagery has historically been of interest mainly for military purposes. The available thermal infrared datasets for evaluating methods addressing these problems are few and the ones that do are not challenging enough for today’s tracking algorithms. Therefore, we hereby propose a thermal infrared dataset for evaluation of short-term tracking methods. The dataset consists of 20 sequences which have been collected from multiple sources and the data format used is in accordance with the Visual Object Tracking (VOT) Challenge.

  • 23.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    A Thermal Object Tracking Benchmark2015Conference paper (Refereed)
    Abstract [en]

    Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. In order to evaluate STSO tracking algorithms on visual imagery, there are de facto standard benchmarks. However, we argue that tracking in thermal imagery is different than in visual imagery, and that a separate benchmark is needed. The available thermal infrared datasets are few and the existing ones are not challenging for modern tracking algorithms. Therefore, we hereby propose a thermal infrared benchmark according to the Visual Object Tracking (VOT) protocol for evaluation of STSO tracking methods. The benchmark includes the new LTIR dataset containing 20 thermal image sequences which have been collected from multiple sources and annotated in the format used in the VOT Challenge. In addition, we show that the ranking of different tracking principles differ between the visual and thermal benchmarks, confirming the need for the new benchmark.

  • 24.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Channel Coded Distribution Field Tracking for Thermal Infrared Imagery2016In: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), IEEE , 2016, p. 1248-1256Conference paper (Refereed)
    Abstract [en]

    We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. The fast progress has been possible thanks to the development of new template-based tracking methods with online template updates, methods which have not been explored for TIR tracking. Instead, tracking methods used for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed. Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a template-based tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. In order to avoid background contamination of the object template, we propose to exploit background information for the online template update and to adaptively select the object region used for tracking. Moreover, we propose a novel method for estimating object scale change. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Further, the proposed tracker, ABCD, and the VOT-TIR2015 winner SRDCFir are evaluated on maritime data. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

  • 25.
    Berg, Amanda
    et al.
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision. Termisk Syst Tekn AB, Diskettgatan 11 B, SE-58335 Linkoping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Syst Tekn AB, Diskettgatan 11 B, SE-58335 Linkoping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Enhanced analysis of thermographic images for monitoring of district heat pipe networks2016In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 83, no 2, p. 215-223Article in journal (Refereed)
    Abstract [en]

    We address two problems related to large-scale aerial monitoring of district heating networks. First, we propose a classification scheme to reduce the number of false alarms among automatically detected leakages in district heating networks. The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps; by (a) using a building segmentation scheme in order to remove detections on buildings, and (b) to use a machine learning approach to classify the remaining detections as true or false leakages. We provide extensive experimental analysis on real-world data, showing that this post-processing step significantly improves the usefulness of the system. Second, we propose a method for characterization of leakages over time, i.e., repeating the image acquisition one or a few years later and indicate areas that suffer from an increased energy loss. We address the problem of finding trends in the degradation of pipe networks in order to plan for long-term maintenance, and propose a visualization scheme exploiting the consecutive data collections.

  • 26.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Generating Visible Spectrum Images from Thermal Infrared2018In: Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1224-1233Conference paper (Refereed)
    Abstract [en]

    Transformation of thermal infrared (TIR) images into visual, i.e. perceptually realistic color (RGB) images, is a challenging problem. TIR cameras have the ability to see in scenarios where vision is severely impaired, for example in total darkness or fog, and they are commonly used, e.g., for surveillance and automotive applications. However, interpretation of TIR images is difficult, especially for untrained operators. Enhancing the TIR image display by transforming it into a plausible, visual, perceptually realistic RGB image presumably facilitates interpretation. Existing grayscale to RGB, so called, colorization methods cannot be applied to TIR images directly since those methods only estimate the chrominance and not the luminance. In the absence of conventional colorization methods, we propose two fully automatic TIR to visual color image transformation methods, a two-step and an integrated approach, based on Convolutional Neural Networks. The methods require neither pre- nor postprocessing, do not require any user input, and are robust to image pair misalignments. We show that the methods do indeed produce perceptually realistic results on publicly available data, which is assessed both qualitatively and quantitatively.

  • 27.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Object Tracking in Thermal Infrared Imagery based on Channel Coded Distribution Fields2017Conference paper (Other academic)
    Abstract [en]

    We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. Tracking methods designed for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed. Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a templatebased tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

  • 28.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Visual Spectrum Image Generation fromThermal Infrared2019Conference paper (Other academic)
    Abstract [en]

    We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. Tracking methods designed for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed. Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a templatebased tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

  • 29.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge2016Conference paper (Other academic)
    Abstract [en]

    The Thermal Infrared Visual Object Tracking (VOT-TIR2015) Challenge was organized in conjunction with ICCV2015. It was the first benchmark on short-term,single-target tracking in thermal infrared (TIR) sequences. The challenge aimed at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. It was based on the VOT2013 Challenge, but introduced the following novelties: (i) the utilization of the LTIR (Linköping TIR) dataset, (ii) adaption of the VOT2013 attributes to thermal data, (iii) a similar evaluation to that of VOT2015. This paper provides an overview of the VOT-TIR2015 Challenge as well as the results of the 24 participating trackers.

  • 30.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Termisk Systemteknik AB, Linköping, Sweden.
    Johnander, Joakim
    Linköping University, Department of Electrical Engineering, Computer Vision. Zenuity AB, Göteborg, Sweden.
    Durand de Gevigney, Flavie
    Linköping University, Department of Electrical Engineering, Computer Vision. Grenoble INP, France.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Semi-automatic Annotation of Objects in Visual-Thermal Video2019Conference paper (Refereed)
    Abstract [en]

    Deep learning requires large amounts of annotated data. Manual annotation of objects in video is, regardless of annotation type, a tedious and time-consuming process. In particular, for scarcely used image modalities human annotationis hard to justify. In such cases, semi-automatic annotation provides an acceptable option.

    In this work, a recursive, semi-automatic annotation method for video is presented. The proposed method utilizesa state-of-the-art video object segmentation method to propose initial annotations for all frames in a video based on only a few manual object segmentations. In the case of a multi-modal dataset, the multi-modality is exploited to refine the proposed annotations even further. The final tentative annotations are presented to the user for manual correction.

    The method is evaluated on a subset of the RGBT-234 visual-thermal dataset reducing the workload for a human annotator with approximately 78% compared to full manual annotation. Utilizing the proposed pipeline, sequences are annotated for the VOT-RGBT 2019 challenge.

  • 31.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Öfjäll, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera2015In: Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Rasmus R. Paulsen; Kim S. Pedersen, Springer, 2015, p. 492-503Conference paper (Refereed)
    Abstract [en]

    We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway. While the problem at a first glance looks similar to road and lane detection, which in the past has been a popular research topic, a closer look reveals that the problem at hand is previously unaddressed. As a consequence, relevant datasets are missing as well, and thus our contribution is two-fold: We propose an approach to the novel problem of obstacle detection on railways and we describe the acquisition of a novel data set.

  • 32.
    Berglin, Lukas
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Design, Evaluation and Implementation of a Pipeline for Semi-Automatic Lung Nodule Segmentation2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Lung cancer is the most common type of cancer in the world and always manifests as lung nodules. Nodules are small tumors that consist of lung tissue. They are usually spherical in shape and their cores can be either solid or subsolid. Nodules are common in lungs, but not all of them are malignant. To determine if a nodule is malignant or benign, attributes like nodule size and volume growth are commonly used. The procedure to obtain these attributes is time consuming, and therefore calls for tools to simplify the process.

    The purpose of this thesis work was to investigate  the feasibility of a semi-automatic lungnodule segmentation pipeline including volume estimation. This was done by implementing, tuning and evaluating image processing algorithms with different characteristics to create pipeline candidates. These candidates were compared using a similarity index between their segmentation results and ground truth markings to determine the most promising one.

    The best performing pipeline consisted of a fixed region of interest together with a level set segmentation algorithm. Its segmentation accuracy was not consistent for all nodules evaluated, but the pipeline showed great potential when dynamically adapting its parameters for each nodule. The use of dynamic parameters was only brie y explored, and further research would be necessary to determine its feasibility.

  • 33.
    Bešenić, Krešimir
    et al.
    Faculty of Electrical Engineering and Computing, University of Zagreb,.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Pandžić, Igor
    Faculty of Electrical Engineering and Computing, University of Zagreb.
    Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation2019In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), SciTePress, 2019, Vol. 5, p. 209-217Conference paper (Refereed)
    Abstract [en]

    Availability of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, datasets with age and gender annotations are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts weakly labeled noisy data. The unsupervised facial biometric data filtering method presented in this paper greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped facial datasets demonstrate the effectiveness of the proposed method, with respect to training and validation scores, training convergence, and generalization capabilities of trained age and gender estimators.

  • 34.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Accurate Tracking by Overlap Maximization2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Visual object tracking is one of the fundamental problems in computer vision, with a wide number of practical applications in e.g.\ robotics, surveillance etc. Given a video sequence and the target bounding box in the first frame, a tracker is required to find the target in all subsequent frames. It is a challenging problem due to the limited training data available. An object tracker is generally evaluated using two criterias, namely robustness and accuracy. Robustness refers to the ability of a tracker to track for long durations, without losing the target. Accuracy, on the other hand, denotes how accurately a tracker can estimate the target bounding box.

    Recent years have seen significant improvement in tracking robustness. However, the problem of accurate tracking has seen less attention. Most current state-of-the-art trackers resort to a naive multi-scale search strategy which has fundamental limitations. Thus, in this thesis, we aim to develop a general target estimation component which can be used to determine accurate bounding box for tracking. We will investigate how bounding box estimators used in object detection can be modified to be used for object tracking. The key difference between detection and tracking is that in object detection, the classes to which the objects belong are known. However, in tracking, no prior information is available about the tracked object, other than a single image provided in the first frame. We will thus investigate different architectures to utilize the first frame information to provide target specific bounding box predictions. We will also investigate how the bounding box predictors can be integrated into a state-of-the-art tracking method to obtain robust as well as accurate tracking.

  • 35.
    Bhat, Goutam
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Incept Inst Artificial Intelligence, U Arab Emirates.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Combining Local and Global Models for Robust Re-detection2018In: 2018 15TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE (AVSS), IEEE , 2018, p. 25-30Conference paper (Refereed)
    Abstract [en]

    Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual tracking. However, these methods still struggle in occlusion and out-of-view scenarios due to the absence of a re-detection component. While such a component requires global knowledge of the scene to ensure robust re-detection of the target, the standard DCF is only trained on the local target neighborhood. In this paper, we augment the state-of-the-art DCF tracking framework with a re-detection component based on a global appearance model. First, we introduce a tracking confidence measure to detect target loss. Next, we propose a hard negative mining strategy to extract background distractors samples, used for training the global model. Finally, we propose a robust re-detection strategy that combines the global and local appearance model predictions. We perform comprehensive experiments on the challenging UAV123 and LTB35 datasets. Our approach shows consistent improvements over the baseline tracker, setting a new state-of-the-art on both datasets.

  • 36.
    Bhat, Goutam
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Johnander, Joakim
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Unveiling the power of deep tracking2018In: Proceedings of the European Conference on Computer Vision (ECCV). 2018., 2018Conference paper (Refereed)
  • 37.
    Bianco, Giuseppe
    et al.
    Lund University, Sweden.
    Ilieva, Mihaela
    Lund University, Sweden; Bulgarian Academic Science, Bulgaria.
    Veibäck, Clas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Öfjäll, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Gadomska, Alicja
    Lund University, Sweden.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Åkesson, Susanne
    Lund University, Sweden.
    Emlen funnel experiments revisited: methods update for studying compass orientation in songbirds2016In: Ecology and Evolution, ISSN 2045-7758, E-ISSN 2045-7758, Vol. 6, no 19, p. 6930-6942Article in journal (Refereed)
    Abstract [en]

    1 Migratory songbirds carry an inherited capacity to migrate several thousand kilometers each year crossing continental landmasses and barriers between distant breeding sites and wintering areas. How individual songbirds manage with extreme precision to find their way is still largely unknown. The functional characteristics of biological compasses used by songbird migrants has mainly been investigated by recording the birds directed migratory activity in circular cages, so-called Emlen funnels. This method is 50 years old and has not received major updates over the past decades. The aim of this work was to compare the results from newly developed digital methods with the established manual methods to evaluate songbird migratory activity and orientation in circular cages. 2 We performed orientation experiments using the European robin (Erithacus rubecula) using modified Emlen funnels equipped with thermal paper and simultaneously recorded the songbird movements from above. We evaluated and compared the results obtained with five different methods. Two methods have been commonly used in songbirds orientation experiments; the other three methods were developed for this study and were based either on evaluation of the thermal paper using automated image analysis, or on the analysis of videos recorded during the experiment. 3 The methods used to evaluate scratches produced by the claws of birds on the thermal papers presented some differences compared with the video analyses. These differences were caused mainly by differences in scatter, as any movement of the bird along the sloping walls of the funnel was recorded on the thermal paper, whereas video evaluations allowed us to detect single takeoff attempts by the birds and to consider only this behavior in the orientation analyses. Using computer vision, we were also able to identify and separately evaluate different behaviors that were impossible to record by the thermal paper. 4 The traditional Emlen funnel is still the most used method to investigate compass orientation in songbirds under controlled conditions. However, new numerical image analysis techniques provide a much higher level of detail of songbirds migratory behavior and will provide an increasing number of possibilities to evaluate and quantify specific behaviors as new algorithms will be developed.

  • 38.
    Biedermann, Daniel
    et al.
    Goethe University, Germany.
    Ochs, Matthias
    Goethe University, Germany.
    Mester, Rudolf
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe University, Germany.
    Evaluating visual ADAS components on the COnGRATS dataset2016In: 2016 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), IEEE , 2016, p. 986-991Conference paper (Refereed)
    Abstract [en]

    We present a framework that supports the development and evaluation of vision algorithms in the context of driver assistance applications and traffic surveillance. This framework allows the creation of highly realistic image sequences featuring traffic scenarios. The sequences are created with a realistic state of the art vehicle physics model; different kinds of environments are featured, thus providing a wide range of testing scenarios. Due to the physically-based rendering technique and variable camera models employed for the image rendering process, we can simulate different sensor setups and provide appropriate and fully accurate ground truth data.

  • 39.
    Björkeson, Felix
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Autonomous Morphometrics using Depth Cameras for Object Classification and Identification2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Identification of individuals has been solved with many different solutions around the world, either using biometric data or external means of verification such as id cards or RFID tags. The advantage of using biometric measurements is that they are directly tied to the individual and are usually unalterable. Acquiring dependable measurements is however challenging when the individuals are uncooperative. A dependable system should be able to deal with this and produce reliable identifications.

    The system proposed in this thesis can autonomously classify uncooperative specimens from depth data. The data is acquired from a depth camera mounted in an uncontrolled environment, where it was allowed to continuously record for two weeks. This requires stable data extraction and normalization algorithms to produce good representations of the specimens. Robust descriptors can therefore be extracted from each sample of a specimen and together with different classification algorithms, the system can be trained or validated. Even with as many as 138 different classes the system achieves high recognition rates. Inspired by the research field of face recognition, the best classification algorithm, the method of fisherfaces, was able to accurately recognize 99.6% of the validation samples. Followed by two variations of the method of eigenfaces, achieving recognition rates of 98.8% and 97.9%. These results affirm that the capabilities of the system are adequate for a commercial implementation.

  • 40.
    Björnfot, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Extension of DIRA (Dual-Energy Iterative Algorithm) to 3D Helical CT2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    There is a need for quantitative CT data in radiation therapy. Currently there are only few algorithms that address this issue, for instance the commercial DirectDensity algorithm. In scientific literature, an example of such an algorithm is DIRA. DIRA is an iterative model-based reconstruction method for dual-energy CT whose goal is to determine the material composition of the patient from accurate linear attenuation coefficients. It has been implemented in a two dimensional geometry, i.e., it could process axial scans only.  There was a need to extend DIRA so that it could process projection data generated in helical scanning geometries. The newly developed algorithm (DIRA-3D) implemented (i) polyenergetic semi-parallel projection generation, (ii) mono-energetic parallel projection generation and (iii) the PI-method for image reconstruction. The computation experiments showed that the accuracies of the resulting LAC and mass fractions were comparable to the ones of the original DIRA. The results converged after 10 iterations.

  • 41.
    Bondemark, Richard
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Improving SLAM on a TOF Camera by Exploiting Planar Surfaces2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Simultaneous localization and mapping (SLAM) is the problem of mapping your surroundings while simultaneously localizing yourself in the map. It is an important and active area of research for robotics. In this master thesis two approaches are attempted to reduce the drift which appears over time in SLAM algorithms. The first approach tries 3 different motion models for the camera. Two of the models exploit the a priori knowledge that the camera is mounted on a trolley. These two methods are shown to improve the results. The second approach attempts to reduce the drift by reducing noise in the point cloud data used for mapping. This is done by finding planar surfaces in the point clouds. Median filtering is used as an alternative to compare the result for noise reduction. The planes estimation approach is also shown to reduce the drift, while the median estimation makes it worse.

  • 42.
    Bradler, Henry
    et al.
    Goethe University of Frankfurt, Germany.
    Anne Wiegand, Birthe
    Goethe University of Frankfurt, Germany.
    Mester, Rudolf
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe University of Frankfurt, Germany.
    The Statistics of Driving Sequences - and what we can learn from them2015In: 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOP (ICCVW), IEEE , 2015, p. 106-114Conference paper (Refereed)
    Abstract [en]

    The motion of a driving car is highly constrained and we claim that powerful predictors can be built that learn the typical egomotion statistics, and support the typical tasks of feature matching, tracking, and egomotion estimation. We analyze the statistics of the ground truth data given in the KITTI odometry benchmark sequences and confirm that a coordinated turn motion model, overlaid by moderate vibrations, is a very realistic model. We develop a predictor that is able to significantly reduce the uncertainty about the relative motion when a new image frame comes in. Such predictors can be used to steer the matching process from frame n to frame n + 1. We show that they can also be employed to detect outliers in the temporal sequence of egomotion parameters.

  • 43.
    Bradler, Henry
    et al.
    Goethe University, Germany.
    Ochs, Matthias
    Goethe University, Germany.
    Fanani, Nolang
    Goethe University, Germany.
    Mester, Rudolf
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe University, Germany.
    Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar geometry and feature correspondences2017In: 2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017), IEEE , 2017, p. 445-453Conference paper (Refereed)
    Abstract [en]

    Traditionally, pose estimation is considered as a two step problem. First, feature correspondences are determined by direct comparison of image patches, or by associating feature descriptors. In a second step, the relative pose and the coordinates of corresponding points are estimated, most often by minimizing the reprojection error (RPE). RPE optimization is based on a loss function that is merely aware of the feature pixel positions but not of the underlying image intensities. In this paper, we propose a sparse direct method which introduces a loss function that allows to simultaneously optimize the unscaled relative pose, as well as the set of feature correspondences directly considering the image intensity values. Furthermore, we show how to integrate statistical prior information on the motion into the optimization process. This constructive inclusion of a Bayesian bias term is particularly efficient in application cases with a strongly predictable (short term) dynamic, e.g. in a driving scenario. In our experiments, we demonstrate that the JET` algorithm we propose outperforms the classical reprojection error optimization on two synthetic datasets and on the KITTI dataset. The JET algorithm runs in real-time on a single CPU thread.

  • 44.
    Brandtberg, Tomas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Saab Dynam AB, Linköping, Sweden.
    Virtual hexagonal and multi-scale operator for fuzzy rank order texture classification using one-dimensional generalised Fourier analysis2016In: 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), IEEE COMPUTER SOC , 2016, p. 2018-2024Conference paper (Refereed)
    Abstract [en]

    This paper presents a study on a family of local hexagonal and multi-scale operators useful for texture analysis. The hexagonal grid shows an attractive rotation symmetry with uniform neighbour distances. The operator depicts a closed connected curve (1D periodic). It is resized within a scale interval during the conversion from the original square grid to the virtual hexagonal grid. Complementary image features, together with their tangential first-order hexagonal derivatives, are calculated. The magnitude/phase information from the Fourier or Fractional Fourier Transform (FFT, FrFT) are accumulated in thirty different Cartesian (polar for visualisation) and multi-scale domains. Simultaneous phase-correlation of a subset of the data gives an estimate of scaling/rotation relative the references. Similarity metrics are used as template matching. The sample, unseen by the system, is classified into the group with the maximum fuzzy rank order. An instantiation of a 12-point hexagonal operator (radius=2) is first successfully evaluated on a set of thirteen Brodatz images (no scaling/rotation). Then it is evaluated on the more challenging KTH-TIPS2b texture dataset (scaling/rotation, varying pose/illumination). A confusion matrix and cumulative fuzzy rank order summaries show, for example, that the correct class is top-ranked 44 - 50% and top-three ranked 68 - 76% of all sample images. A similar evaluation, using a box-like 12-point mask of square grids, gives overall lower accuracies. Finally, the FrFT parameter is an additional tuning parameter influencing the accuracies significantly.

  • 45.
    Brorsson, Andreas
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Compressive Sensing: Single Pixel SWIR Imaging of Natural Scenes2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Photos captured in the shortwave infrared (SWIR) spectrum are interesting in military applications because they are independent of what time of day the pic- ture is captured because the sun, moon, stars and night glow illuminate the earth with short-wave infrared radiation constantly. A major problem with today’s SWIR cameras is that they are very expensive to produce and hence not broadly available either within the military or to civilians. Using a relatively new tech- nology called compressive sensing (CS), enables a new type of camera with only a single pixel sensor in the sensor (a SPC). This new type of camera only needs a fraction of measurements relative to the number of pixels to be reconstructed and reduces the cost of a short-wave infrared camera with a factor of 20. The camera uses a micromirror array (DMD) to select which mirrors (pixels) to be measured in the scene, thus creating an underdetermined linear equation system that can be solved using the techniques described in CS to reconstruct the im- age. Given the new technology, it is in the Swedish Defence Research Agency (FOI) interest to evaluate the potential of a single pixel camera. With a SPC ar- chitecture developed by FOI, the goal of this thesis was to develop methods for sampling, reconstructing images and evaluating their quality. This thesis shows that structured random matrices and fast transforms have to be used to enable high resolution images and speed up the process of reconstructing images signifi- cantly. The evaluation of the images could be done with standard measurements associated with camera evaluation and showed that the camera can reproduce high resolution images with relative high image quality in daylight.

  • 46.
    Budde, Kiran Kumar
    Linköping University, Department of Electrical Engineering, Computer Vision.
    A Matlab Toolbox for fMRI Data Analysis: Detection, Estimation and Brain Connectivity2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Functional Magnetic Resonance Imaging (fMRI) is one of the best techniques for neuroimaging and has revolutionized the way to understand the brain functions. It measures the changes in the blood oxygen level-dependent (BOLD) signal which is related to the neuronal activity. Complexity of the data, presence of different types of noises and the massive amount of data makes the fMRI data analysis a challenging one. It demands efficient signal processing and statistical analysis methods.  The inference of the analysis is used by the physicians, neurologists and researchers for better understanding of the brain functions.

         The purpose of this study is to design a toolbox for fMRI data analysis. It includes methods to detect the brain activity maps, estimation of the hemodynamic response (HDR) and the connectivity of the brain structures. This toolbox provides methods for detection of activated brain regions measured with Bayesian estimator. Results are compared with the conventional methods such as t-test, ordinary least squares (OLS) and weighted least squares (WLS). Brain activation and HDR are estimated with linear adaptive model and nonlinear method based on radial basis function (RBF) neural network. Nonlinear autoregressive with exogenous inputs (NARX) neural network is developed to model the dynamics of the fMRI data.  This toolbox also provides methods to brain connectivity such as functional connectivity and effective connectivity.  These methods are examined on simulated and real fMRI datasets.

  • 47.
    Carlsson, Mattias
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Neural Networks for Semantic Segmentation in the Food Packaging Industry2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Industrial applications of computer vision often utilize traditional image processing techniques whereas state-of-the-art methods in most image processing challenges are almost exclusively based on convolutional neural networks (CNNs). Thus there is a large potential for improving the performance of many machine vision applications by incorporating CNNs.

    One such application is the classification of juice boxes with straws, where the baseline solution uses classical image processing techniques on depth images to reject or accept juice boxes. This thesis aim to investigate how CNNs perform on the task of semantic segmentation (pixel-wise classification) of said images and if the result can be used to increase classification performance.

    A drawback of CNNs is that they usually require large amounts of labelled data for training to be able to generalize and learn anything useful. As labelled data is hard to come by, two ways to get cheap data are investigated, one being synthetic data generation and the other being automatic labelling using the baseline solution.

    The implemented network performs well on semantic segmentation, even when trained on synthetic data only, though the performance increases with the ratio of real (automatically labelled) to synthetic images. The classification task is very sensitive to small errors in semantic segmentation and the results are therefore not as good as the baseline solution. It is suspected that the drop in performance between validation and test data is due to a domain shift between the data sets, e.g. variations in data collection and straw and box type, and fine-tuning to the target domain could definitely increase performance.

    When trained on synthetic data the domain shift is even larger and the performance on classification is next to useless. It is likely that the results could be improved by using more advanced data generation, e.g. a generative adversarial network (GAN), or more rigorous modelling of the data.

  • 48.
    Ceco, Ema
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Image Analysis in the Field of Oil Contamination Monitoring2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Monitoring wear particles in lubricating oils allows specialists to evaluate thehealth and functionality of a mechanical system. The main analysis techniquesavailable today are manual particle analysis and automatic optical analysis. Man-ual particle analysis is effective and reliable since the analyst continuously seeswhat is being counted . The drawback is that the technique is quite time demand-ing and dependent of the skills of the analyst. Automatic optical particle countingconstitutes of a closed system not allowing for the objects counted to be observedin real-time. This has resulted in a number of sources of error for the instrument.In this thesis a new method for counting particles based on light microscopywith image analysis is proposed. It has proven to be a fast and effective methodthat eliminates the sources of error of the previously described methods. Thenew method correlates very well with manual analysis which is used as a refer-ence method throughout this study. Size estimation of particles and detectionof metallic particles has also shown to be possible with the current image analy-sis setup. With more advanced software and analysis instrumentation, the imageanalysis method could be further developed to a decision based machine allowingfor declarations about which wear mode is occurring in a mechanical system.

  • 49.
    Chandaria, Jigna
    et al.
    BBC Research, UK.
    Thomas, Graham
    BBC Research, UK.
    Bartczak, Bogumil
    University of Kiel, Germany.
    Koeser, Kevin
    University of Kiel, Germany.
    Koch, Reinhard
    University of Kiel, Germany.
    Becker, Mario
    Fraunhofer IGD, Germany.
    Bleser, Gabriele
    Fraunhofer IGD, Germany.
    Stricker, Didier
    Fraunhofer IGD, Germany.
    Wohlleber, Cedric
    Fraunhofer IGD, Germany.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hol, Jeroen
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skoglund, Johan
    Linköping University, The Institute of Technology.
    Slycke, Per
    Xsens, Netherlands.
    Smeitz, Sebastiaan
    Xsens, Netherlands.
    Real-Time Camera Tracking in the MATRIS Project2006In: Prcoeedings of the 2006 International Broadcasting Convention, 2006Conference paper (Refereed)
    Abstract [en]

    In order to insert a virtual object into a TV image, the graphics system needs to know precisely how the camera is moving, so that the virtual object can be rendered in the correct place in every frame. Nowadays this can be achieved relatively easily in postproduction, or in a studio equipped with a special tracking system. However, for live shooting on location, or in a studio that is not specially equipped, installing such a system can be difficult or uneconomic. To overcome these limitations, the MATRIS project is developing a real-time system for measuring the movement of a camera. The system uses image analysis to track naturally occurring features in the scene, and data from an inertial sensor. No additional sensors, special markers, or camera mounts are required. This paper gives an overview of the system and presents some results.  

  • 50.
    Chellappa, Rama
    et al.
    Department of Electrical and Computer Engineering, University of Maryland, USA.
    Heyden, AndersLund University, Sweden.Laurendeau, DenisUniversité Laval, Canada.Felsberg, MichaelLinköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).Borga, MagnusLinköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Arts and Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Special issue on ICPR 2014 awarded papers2016Collection (editor) (Refereed)
    Abstract [en]

    We, the Guest Editors of this special issue of Pattern Recognition Letters are pleased to share these contributions with you. The papers included here are based on work from the 22nd International Conference on Pattern Recognition (IAPR) in Stockholm, Sweden, held August 24–28, 2014. The papers selected for this special issue were those winning one of the IAPR awards, as well as one paper by a former student of the winner of the KS Fu Prize, Prof. Jitendra Malik. Taken together, this body of work represents some of the finest research being conducted by the IAPR community worldwide, it builds on a rich legacy of accomplishment by the entire community, and it offers a view to the future, to where we are going as a scientific community.

    For each of the award-winning papers, the authors were asked to revise and extend their contributions to full journal length and to provide true added value vis-à-vis the original conference submission. In some cases, the authors elected to modify the titles slightly, and in some cases the list of authors has also been modified. The resulting manuscripts were sent out for full review by a different set of referees than those who reviewed the conference versions. The process, including required revisions, was in accordance with the standing editorial policy of Pattern Recognition Letters, resulting in the final versions accepted and appearing here. These are thoroughly vetted, high-caliber scientific contributions.

    It has been our honor to serve as Guest Editors for this special issue. We would like to thank the Editors of Pattern Recognition Letters for allowing us this opportunity. We are especially grateful to Dr. Gabriella Sanniti di Baja for her enthusiasm, support, and her willingness to keep prodding us along to bring the special issue through to completion. We would also like to thank all of those who reviewed the papers, both originally for the conference and subsequently for the journal, and those who served on the ICPR awards and KS Fu Prize committees.

    Finally, we express our heartfelt gratitude to all of the authors for taking the time to prepare these versions for our collective enlightenment, sharing their knowledge, innovation, and discoveries with the rest of us.

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