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  • 51.
    Gogic, Ivan
    et al.
    Univ Zagreb, Croatia.
    Manhart, Martina
    Univ Zagreb, Croatia.
    Pandzic, Igor S.
    Univ Zagreb, Croatia.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Fast facial expression recognition using local binary features and shallow neural networks2020In: The Visual Computer, ISSN 0178-2789, E-ISSN 1432-2315, Vol. 36, no 1, p. 97-112Article in journal (Refereed)
    Abstract [en]

    Facial expression recognition applications demand accurate and fast algorithms that can run in real time on platforms with limited computational resources. We propose an algorithm that bridges the gap between precise but slow methods and fast but less precise methods. The algorithm combines gentle boost decision trees and neural networks. The gentle boost decision trees are trained to extract highly discriminative feature vectors (local binary features) for each basic facial expression around distinct facial landmark points. These sparse binary features are concatenated and used to jointly optimize facial expression recognition through a shallow neural network architecture. The joint optimization improves the recognition rates of difficult expressions such as fear and sadness. Furthermore, extensive experiments in both within- and cross-database scenarios have been conducted on relevant benchmark data sets for facial expression recognition: CK+, MMI, JAFFE, and SFEW 2.0. The proposed method (LBF-NN) compares favorably with state-of-the-art algorithms while achieving an order of magnitude improvement in execution time.

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  • 52.
    Gonzalez, Santiago A. Rodriguez
    et al.
    National University of Cordoba (UNC),Cordoba,Argentina.
    Shimoni, Michal
    Signal and Image Centre SIC-RMA, Brussels, Belgium.
    Plaza, Javier
    Hyperspectral Computing Laboratory, University of Extremadura, Cáceres,Spain.
    Plaza, Antonio
    Hyperspectral Computing Laboratory, University of Extremadura, Cáceres,Spain.
    Renhorn, Ingmar
    Glana Sensors AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    The Detection of Concealed Targets in Woodland Areas using Hyperspectral Imagery2020In: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile: IEEE , 2020, p. 451-455Conference paper (Refereed)
    Abstract [en]

    Recent innovations in microelectronic and semiconductor technology enable the creation of smaller and economical hyperspectral cameras. A filter combined camera with advanced scanning module is a game changer that extends the application of miniature hyperspectral imagers to many security domains. This work presents the assessment of the imager L4 from Glana Sensors to detect concealed targets in woodland areas. Several target detection methods were applied to a collection of scenes acquired under various illumination conditions and containing different materials. The potential and limitations of this new imaging device in the context of difficult target detection in forested area are evaluated and discussed.

  • 53.
    Hamoir, Dominique
    et al.
    Onera – The French Aerospace Lab, Toulouse, France.
    Hespel, Laurent
    Onera – The French Aerospace Lab, Toulouse, France.
    Déliot, Philippe
    Onera – The French Aerospace Lab, Toulouse, France.
    Boucher, Yannick
    Onera – The French Aerospace Lab, Toulouse, France.
    Steinvall, Ove
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Ahlberg, Jörgen
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Larsson, Håkan
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Letalick, Dietmar
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Lutzmann, Peter
    Fraunhofer-IOSB, Ettlingen, Germany.
    Repasi, Endre
    Fraunhofer-IOSB, Ettlingen, Germany.
    Ritt, Gunnar
    Fraunhofer-IOSB, Ettlingen, Germany.
    Results of ACTIM: an EDA study on spectral laser imaging2011In: Proc. SPIE 8186, Electro-Optical Remote Sensing, Photonic Technologies, and Applications V / [ed] Gary W. Kamerman; Ove Steinvall; Gary J. Bishop; John D. Gonglewski; Keith L. Lewis; Richard C. Hollins; Thomas J. Merlet, SPIE - International Society for Optical Engineering, 2011, p. Art.nr 8186A-25-Conference paper (Refereed)
    Abstract [en]

    The European Defence Agency (EDA) launched the Active Imaging (ACTIM) study to investigate the potential of active imaging, especially that of spectral laser imaging. The work included a literature survey, the identification of promising military applications, system analyses, a roadmap and recommendations.   Passive multi- and hyper-spectral imaging allows discriminating between materials. But the measured radiance in the sensor is difficult to relate to spectral reflectance due to the dependence on e.g. solar angle, clouds, shadows... In turn, active spectral imaging offers a complete control of the illumination, thus eliminating these effects. In addition it allows observing details at long ranges, seeing through degraded atmospheric conditions, penetrating obscurants (foliage, camouflage…) or retrieving polarization information. When 3D, it is suited to producing numerical terrain models and to performing geometry-based identification. Hence fusing the knowledge of ladar and passive spectral imaging will result in new capabilities.  We have identified three main application areas for active imaging, and for spectral active imaging in particular: (1) long range observation for identification, (2) mid-range mapping for reconnaissance, (3) shorter range perception for threat detection. We present the system analyses that have been performed for confirming the interests, limitations and requirements of spectral active imaging in these three prioritized applications.

  • 54.
    Hatami, Sepehr
    et al.
    Swerea IVF AB, Mölndal, Sweden.
    Dahl-Jendelin, Anton
    Swerea IVF AB, Mölndal, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Nelsson, Claes
    Termisk Systemteknik AB, Linköping, Sweden.
    Selective Laser Melting Process Monitoring by Means of Thermography2018In: Proceedings of Euro Powder Metallurgy Congress (Euro PM), European Powder Metallurgy Association (EPMA) , 2018, article id 3957771Conference paper (Refereed)
    Abstract [en]

    Selective laser melting (SLM) enables production of highly intricate components. From this point of view, the capabilities of this technology are known to the industry and have been demonstrated in numerous applications. Nonetheless, for serial production purposes the manufacturing industry has so far been reluctant in substituting its conventional methods with SLM. One underlying reason is the lack of simple and reliable process monitoring methods. This study examines the feasibility of using thermography for process monitoring. To this end, an infra-red (IR) camera was mounted off-axis to monitor and record the temperature of every layer. The recorded temperature curves are analysed and interpreted with respect to different stages of the process. Furthermore, the possibility of detecting variations in laser settings by means of thermography is demonstrated. The results show that once thermal patterns are identified, this data can be utilized for in-process and post-process monitoring of SLM production.

  • 55.
    Heggenes, Jan
    et al.
    Department of Environmental and Health Sciences, University College of Southeast Norway, Bø i Telemark, Norway.
    Odland, Arvid
    Department of Environmental and Health Sciences, University College of Southeast Norway, Bø i Telemark, Norway.
    Chevalier, Tomas
    Scienvisic AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Berg, Amanda
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Larsson, Håkan
    Swedish Defence Research Agency, Sweden.
    Bjerketvedt, Dag
    Department of Environmental and Health Sciences, University College of Southeast Norway, Bø i Telemark, Norway.
    Herbivore grazing—or trampling? Trampling effects by a large ungulate in cold high-latitude ecosystems2017In: Ecology and Evolution, E-ISSN 2045-7758, Vol. 7, no 16, p. 6423-6431Article in journal (Refereed)
    Abstract [en]

    Mammalian herbivores have important top-down effects on ecological processes and landscapes by generating vegetation changes through grazing and trampling. For free-ranging herbivores on large landscapes, trampling is an important ecological factor. However, whereas grazing is widely studied, low-intensity trampling is rarely studied and quantified. The cold-adapted northern tundra reindeer (Rangifer tarandus) is a wide-ranging keystone herbivore in large open alpine and Arctic ecosystems. Reindeer may largely subsist on different species of slow-growing ground lichens, particularly in winter. Lichen grows in dry, snow-poor habitats with frost. Their varying elasticity makes them suitable for studying trampling. In replicated factorial experiments, high-resolution 3D laser scanning was used to quantify lichen volume loss from trampling by a reindeer hoof. Losses were substantial, that is, about 0.3 dm3 per imprint in dry thick lichen, but depended on type of lichen mat and humidity. Immediate trampling volume loss was about twice as high in dry, compared to humid thin (2–3 cm), lichen mats and about three times as high in dry vs. humid thick (6–8 cm) lichen mats, There was no significant difference in volume loss between 100% and 50% wetted lichen. Regained volume with time was insignificant for dry lichen, whereas 50% humid lichen regained substantial volumes, and 100% humid lichen regained almost all lost volume, and mostly within 10–20 min. Reindeer trampling may have from near none to devastating effects on exposed lichen forage. During a normal week of foraging, daily moving 5 km across dry 6- to 8-cm-thick continuous lichen mats, one adult reindeer may trample a lichen volume corresponding to about a year's supply of lichen. However, the lichen humidity appears to be an important factor for trampling loss, in addition to the extent of reindeer movement.

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  • 56.
    Horney, Tobias
    et al.
    Swedish Defence Research Agency, Sweden.
    Ahlberg, Jörgen
    Swedish Defence Research Agency, Sweden.
    Grönwall, Christina
    Swedish Defence Research Agency, Sweden.
    Folkesson, Martin
    Swedish Defence Research Agency, Sweden.
    Silvervarg, Karin
    Swedish Defence Research Agency, Sweden.
    Fransson, Jörgen
    Swedish Defence Research Agency, Sweden.
    Klasén, Lena
    Swedish Defence Research Agency, Sweden.
    Jungert, Erland
    Swedish Defence Research Agency, Sweden.
    Lantz, Fredrik
    Swedish Defence Research Agency, Sweden.
    Ulvklo, Morgan
    Swedish Defence Research Agency, Sweden.
    An information system for target recognition2004In: Volume 5434 Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications / [ed] Belur V. Dasarathy, SPIE - International Society for Optical Engineering, 2004, p. 163-175Conference paper (Refereed)
    Abstract [en]

    We present an approach to a general decision support system. The aim is to cover the complete process for automatic target recognition, from sensor data to the user interface. The approach is based on a query-based information system, and include tasks like feature extraction from sensor data, data association, data fusion and situation analysis. Currently, we are working with data from laser radar, infrared cameras, and visual cameras, studying target recognition from cooperating sensors on one or several platforms. The sensors are typically airborne and at low altitude. The processing of sensor data is performed in two steps. First, several attributes are estimated from the (unknown but detected) target. The attributes include orientation, size, speed, temperature etc. These estimates are used to select the models of interest in the matching step, where the target is matched with a number of target models, returning a likelihood value for each model. Several methods and sensor data types are used in both steps. The user communicates with the system via a visual user interface, where, for instance, the user can mark an area on a map and ask for hostile vehicles in the chosen area. The user input is converted to a query in ΣQL, a query language developed for this type of applications, and an ontological system decides which algorithms should be invoked and which sensor data should be used. The output from the sensors is fused by a fusion module and answers are given back to the user. The user does not need to have any detailed technical knowledge about the sensors (or which sensors that are available), and new sensors and algorithms can easily be plugged into the system.

  • 57.
    Ingemars, Nils
    et al.
    Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
    Feature-based Face Tracking using Extended Kalman Filtering2007Conference paper (Other academic)
    Abstract [en]

    This work examines the possiblity to, with the computational power of today’s consumer hardware, employ techniques previously developed for 3D tracking of rigid objects, and use them for tracking of deformable objects. Our target objects are human faces in a video conversation pose, and our purpose is to create a deformable face tracker based on a head tracker operating in real-time on consumer hardware. We also investigate how to combine model-based and image based tracking in order to get precise tracking and avoid drift.

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  • 58.
    Larsen, Martin
    et al.
    FFI, Norwegian Defence Research Establishment, Norway.
    Rolfsfjord, Sigmund
    FFI, Norwegian Defence Research Establishment, Norway.
    Gusland, Daniel
    FFI, Norwegian Defence Research Establishment, Norway.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. University of Oslo, Norway.
    Mathiassen, Kim
    FFI, Norwegian Defence Research Establishment, Norway.
    BASE: Probably a Better Approach to Visual Multi-Object Tracking2024In: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy, 2024, SciTePress, 2024, p. 110-121Conference paper (Refereed)
    Abstract [en]

    The field of visual object tracking is dominated by methods that combine simple tracking algorithms and ad hoc schemes. Probabilistic tracking algorithms, which are leading in other fields, are surprisingly absent from the leaderboards. We found that accounting for distance in target kinematics, exploiting detector confidence and modelling non-uniform clutter characteristics is critical for a probabilistic tracker to work in visual tracking. Previous probabilistic methods fail to address most or all these aspects, which we believe is why they fall so far behind current state-of-the-art (SOTA) methods (there are no probabilistic trackers in the MOT17 top 100). To rekindle progress among probabilistic approaches, we propose a set of pragmatic models addressing these challenges, and demonstrate how they can be incorporated into a probabilistic framework. We present BASE (Bayesian Approximation Single-hypothesis Estimator), a simple, performant and easily extendible visual tracker, achieving state-of-the-art (SOTA) on MOT17 and MOT20, without using Re-Id. Code available at https://github.com/ffi-no/paper-base-visapp-2024.

  • 59.
    Markus, Nenad
    et al.
    University of Zagreb, Croatia .
    Frljak, Miroslav
    University of Zagreb, Croatia .
    Pandzic, Igor S.
    University of Zagreb, Croatia .
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Eye pupil localization with an ensemble of randomized trees2014In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 47, no 2, p. 578-587Article in journal (Refereed)
    Abstract [en]

    We describe a method for eye pupil localization based on an ensemble of randomized regression trees and use several publicly available datasets for its quantitative and qualitative evaluation. The method compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices.

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  • 60.
    Markus, Nenad
    et al.
    Faculty of Electrical Engineering and Computing, University of Zagreb.
    Gogic, Ivan
    Faculty of Electrical Engineering and Computing, University of Zagreb.
    Pandžic, Igor
    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.
    Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment2018In: Proceedings of BMVC 2018 and Workshops, Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition , 2018, p. 1-11, article id 896Conference paper (Refereed)
    Abstract [en]

    Ren et al. [17] recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.

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    Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
  • 61.
    Markus, Nenad
    et al.
    University of Zagreb, Croatia.
    Pandzic, Igor S.
    University of Zagreb, Croatia.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion2016In: 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), IEEE COMPUTER SOC , 2016, p. 2380-2385Conference paper (Refereed)
    Abstract [en]

    Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly-labeled data.

  • 62.
    Markus, Nenad
    et al.
    Univ Zagreb, Croatia.
    Pandzic, Igor S.
    Univ Zagreb, Croatia.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning From Unlabeled Videos and 3D-Shape Retrieval2019In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 28, no 1, p. 279-290Article in journal (Refereed)
    Abstract [en]

    Current best local descriptors are learned on a large data set of matching and non-matching keypoint pairs. However, data of this kind are not always available, since the detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly labeled data. In addition, we discuss how to improve the method by incorporating the procedure of mining hard negatives. We also show how our approach can be used to learn convolutional features from unlabeled video signals and 3D models.

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  • 63.
    Markuš, Nenad
    et al.
    University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
    Fratarcangeli, Marco
    Chalmers University of Technology, Dept. of Applied Information Technology, Göteborg, Sweden.
    Pandžić, Igor
    University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Fast Rendering of Image Mosaics and ASCII Art2015In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 34, no 6, p. 251-261Article in journal (Refereed)
    Abstract [en]

    An image mosaic is an assembly of a large number of small images, usually called tiles, taken from a specific dictionary/codebook. When viewed as a whole, the appearance of a single large image emerges, i.e. each tile approximates a small block of pixels. ASCII art is a related (and older) graphic design technique for producing images from printable characters. Although automatic procedures for both of these visualization schemes have been studied in the past, some are computationally heavy and cannot offer real-time and interactive performance. We propose an algorithm able to reproduce the quality of existing non-photorealistic rendering techniques, in particular ASCII art and image mosaics, obtaining large performance speed-ups. The basic idea is to partition the input image into a rectangular grid and use a decision tree to assign a tile from a pre-determined codebook to each cell. Our implementation can process video streams from webcams in real time and it is suitable for modestly equipped devices. We evaluate our technique by generating the renderings of a variety of images and videos, with good results. The source code of our engine is publicly available.

  • 64.
    Markuš, Nenad
    et al.
    University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
    Frljak, Miroslav
    University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
    Pandžić, Igor
    University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Forchheimer, Robert
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
    High-performance face tracking2012Conference paper (Refereed)
    Abstract [en]

    Face tracking is an extensively studied field. Nevertheless, it is still a challenge to make a robust and efficient face tracker, especially on mobile devices. This extended abstract briefly describes our implementation of a high-performance multi-platform face and facial feature tracking system. The main characteristics of our approach are that the tracker is fully automatic and works with the majority of faces without any manual initialization. It is robust, resistant to rapid changes in pose and facial expressions, does not suffer from drifting and is modestly computationally expensive. The tracker runs in real-time on mobile devices.

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  • 65.
    Nawaz, Tahir
    et al.
    Computational Vision Group, Department of Computer Science, University of Reading.
    Berg, Amanda
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Ferryman, James
    Computational Vision Group, Department of Computer Science, University of Reading.
    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.
    Effective evaluation of privacy protection techniques in visible and thermal imagery2017In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 26, no 5, article id 051408Article in journal (Refereed)
    Abstract [en]

    Privacy protection may be defined as replacing the original content in an image region with a new (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed the development of privacy protection techniques needs also to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgements or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. This work proposes a new annotation-free evaluation method that is neither subjective nor assumes a specific target type. It assesses two key aspects of privacy protection: protection and utility. Protection is quantified as an appearance similarity and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences) including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset, called TST-Priv, is made available online below for community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques, and also show comparisons of the proposed method over existing methods.

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  • 66.
    Ringaby, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Ahlberg, Jörgen
    Sensor Informatics Group, Swedish Defence Research Agenc y (FOI), Linköping.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wadströmer, Niclas
    Sensor Informatics Group, Swedish Defence Research Agenc y (FOI), Linköping.
    Co-alignmnent of Aerial Push-broom Strips using Trajectory Smoothness Constraints2010Conference paper (Other academic)
    Abstract [en]

    We study the problem of registering a sequence of scan lines (a strip) from an airborne push-broom imager to another sequence partly covering the same area. Such a registration has to compensate for deformations caused by attitude and speed changes in the aircraft. The registration is challenging, as both strips contain such deformations. Our algorithm estimates the 3D rotation of the camera for each scan line, by parametrising it as a linear spline with a number of knots evenly distributed in one of the strips. The rotations are estimated from correspondences between strips of the same area. Once the rotations are known, they can be compensated for, and each line of pixels can be transformed such that ground trace of the two strips are registered with respect to each other.

  • 67.
    Ringaby, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Ahlberg, Jörgen
    FOI, Swedish Defence Research Agency, Linköping, Sweden.
    Wadströmer, Niclas
    FOI, Swedish Defence Research Agency, Linköping, Sweden.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Co-aligning Aerial Hyperspectral Push-broom Strips for Change Detection2010In: Proc. SPIE 7835, Electro-Optical Remote Sensing, Photonic Technologies, and Applications IV / [ed] Gary W. Kamerman; Ove Steinvall; Keith L. Lewis; Richard C. Hollins; Thomas J. Merlet; Gary J. Bishop; John D. Gonglewski, SPIE - International Society for Optical Engineering, 2010, p. Art.nr. 7835B-36-Conference paper (Refereed)
    Abstract [en]

    We have performed a field trial with an airborne push-broom hyperspectral sensor, making several flights over the same area and with known changes (e.g., moved vehicles) between the flights. Each flight results in a sequence of scan lines forming an image strip, and in order to detect changes between two flights, the two resulting image strips must be geometrically aligned and radiometrically corrected. The focus of this paper is the geometrical alignment, and we propose an image- and gyro-based method for geometric co-alignment (registration) of two image strips. The method is particularly useful when the sensor is not stabilized, thus reducing the need for expensive mechanical stabilization. The method works in several steps, including gyro-based rectification, global alignment using SIFT matching, and a local alignment using KLT tracking. Experimental results are shown but not quantified, as ground truth is, by the nature of the trial, lacking.

  • 68.
    Runnemalm, Anna
    et al.
    Division of Engineering Science, University West, Trollättan, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Appelgren, Anders
    Division of Engineering Science, University West, Trollättan, Sweden.
    Sjökvist, Stefan
    Termisk Systemteknik AB, Linköping, Sweden.
    Automatic Inspection of Spot Welds by Thermography2014In: Journal of nondestructive evaluation, ISSN 0195-9298, E-ISSN 1573-4862, Vol. 33, no 3, p. 398-406Article in journal (Refereed)
    Abstract [en]

    The interest for thermography as a method for spot weld inspection has increased during the last years since it is a full-field method suitable for automatic inspection. Thermography systems can be developed in different ways, with different physical setups, excitation sources, and image analysis algorithms. In this paper we suggest a single-sided setup of a thermography system using a flash lamp as excitation source. The analysis algorithm aims to find the spatial region in the acquired images corresponding to the successfully welded area, i.e., the nugget size. Experiments show that the system is able to detect spot welds, measure the nugget diameter, and based on the information also separate a spot weld from a stick weld. The system is capable to inspect more than four spot welds per minute, and has potential for an automatic non-destructive system for spot weld inspection. The development opportunities are significant, since the algorithm used in the initial analysis is rather simplified. Moreover, further evaluation of alternative excitation sources can potentially improve the performance.

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  • 69.
    Shimoni, Michal
    et al.
    Signal and Image Centre, Dept. of Electrical Engineering (SIC-RMA), Brussels, Belgium.
    Tolt, Gustav
    FOI Swedish Defence Research Agency, Linköping, Sweden.
    Perneel, Christiaan
    Dept. of Mathematics, Royal Military Academy, Brussels, Belgium.
    Ahlberg, Jörgen
    FOI Swedish Defence Research Agency, Linköping, Sweden.
    Detection of vehicles in shadow areas2011In: 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE , 2011, p. 1-4Conference paper (Refereed)
    Abstract [en]

    This paper presents a new method to automatically detect occluded vehicle in semi or deep shadow areas using combined very high resolution (VHR) 3D LIDAR and hyperspectral data. The proposed shape/spectral integration (SSI) decision fusion algorithm was shown to outperform the spectral based anomaly algorithm mainly in deep shadow areas. The fusion of LIDAR DSM data with spectral data is useful in the detection of vehicles in semi and deep shadow areas. The utility of shape information was shown to be a way to enhance spectral target detection in complex urban scene.

  • 70.
    Shimoni, Michal
    et al.
    Signal and Image Centre, Dept. of Electrical Engineering (SIC-RMA), Brussels, Belgium.
    Tolt, Gustav
    FOI Swedish Defence Research Agency, Linköping, Sweden.
    Perneel, Christiaan
    Dept. of Mathematics, Royal Military Academy, Brussels, Belgium.
    Ahlberg, Jörgen
    FOI Swedish Defence Research Agency, Linköping, Sweden.
    Detection of vehicles in shadow areas using combined hyperspectral and LIDAR data2011In: 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE , 2011, p. 4427-4430Conference paper (Refereed)
    Abstract [en]

    In an effort to overcome the limitations of small target detection in complex urban scene, complementary data sets are combined to provide additional insight about a particular scene. This paper presents a method based on shape/spectral integration (SSI) decision level fusion algorithm to improve the detection of vehicles in semi and deep shadow areas. A four steps process combines high resolution LIDAR and hyperspectral data to classify shadow areas, segment vehicles in LIDAR data, detect spectral anomalies and improves vehicle detection. The SSI decision level fusion algorithm was shown to outperform detection using a single data set and the utility of shape information was shown to be a way to enhance spectral target detection in complex urban scenes.  

  • 71.
    Steinvall, Ove
    et al.
    Swedish Defense Research Agency (FOI), Sweden.
    Renhorn, Ingmar
    Swedish Defense Research Agency (FOI), Sweden.
    Ahlberg, Jörgen
    Swedish Defense Research Agency (FOI), Sweden.
    Larsson, Håkan
    Swedish Defense Research Agency (FOI), Sweden.
    Letalick, Dietmar
    Swedish Defense Research Agency (FOI), Sweden.
    Repasi, Endre
    Fraunhofer IOSB, Germany.
    Lutzmann, Peter
    Fraunhofer IOSB, Germany.
    Anstett, Gregor
    Fraunhofer IOSB, Germany.
    Hamoir, Dominique
    Onéra, France.
    Hespel, Laurent
    Onéra, France.
    Boucher, Yannick
    Onéra, France.
    ACTIM: An EDA initiated study on spectral active imaging2010In: Proc. SPIE 7835, Electro-Optical Remote Sensing, Photonic Technologies, and Applications IV / [ed] Gary W. Kamerman; Ove Steinvall; Keith L. Lewis; Richard C. Hollins; Thomas J. Merlet; Gary J. Bishop; John D. Gonglewski, SPIE - International Society for Optical Engineering, 2010, Vol. 7835, p. Art.nr. 7835A-12-Conference paper (Refereed)
    Abstract [en]

    This paper will describe ongoing work from an EDA initiated study on Active Imaging with emphasis of using multi or broadband spectral lasers and receivers. Present laser based imaging and mapping systems are mostly based on a fixed frequency lasers. On the other hand great progress has recently occurred in passive multi- and hyperspectral imaging with applications ranging from environmental monitoring and geology to mapping, military surveillance, and reconnaissance. Data bases on spectral signatures allow the possibility to discriminate between different materials in the scene. Present multi- and hyperspectral sensors mainly operate in the visible and short wavelength region (0.4-2.5 μm) and rely on the solar radiation giving shortcoming due to shadows, clouds, illumination angles and lack of night operation. Active spectral imaging however will largely overcome these difficulties by a complete control of the illumination. Active illumination enables spectral night and low-light operation beside a robust way of obtaining polarization and high resolution 2D/3D information.  Recent development of broadband lasers and advanced imaging 3D focal plane arrays has led to new opportunities for advanced spectral and polarization imaging with high range resolution. Fusing the knowledge of ladar and passive spectral imaging will result in new capabilities in the field of EO-sensing to be shown in the study. We will present an overview of technology, systems and applications for active spectral imaging and propose future activities in connection with some prioritized applications. 

  • 72.
    Tolt, Gustav
    et al.
    FOI (Swedish Defence Research Agency), Linköping, Sweden.
    Shimoni, Michal
    Signal and Image Centre, Dept. of Electrical Engineering (SIC-RMA), Brussels, Belgium.
    Ahlberg, Jörgen
    FOI (Swedish Defence Research Agency), Linköping, Sweden.
    A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data2011In: 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE , 2011, p. 4423-4426Conference paper (Refereed)
    Abstract [en]

    In this paper, a shadow detection method combining hyperspectral and LIDAR data analysis is presented. First, a rough shadow image is computed through line-of-sight analysis on a Digital Surface Model (DSM), using an estimate of the position of the sun at the time of image acquisition. Then, large shadow and non-shadow areas in that image are detected and used for training a supervised classifier (a Support Vector Machine, SVM) that classifies every pixel in the hyperspectral image as shadow or nonshadow. Finally, small holes are filled through image morphological analysis. The method was tested on data including a 24 band hyperspectral image in the VIS/NIR domain (50 cm spatial resolution) and a DSM of 25 cm resolution. The results were in good accordance with visual interpretation. As the line-of-sight analysis step is only used for training, geometric mismatches (about 2 m) between LIDAR and hyperspectral data did not affect the results significantly, nor did uncertainties regarding the position of the sun.

  • 73.
    Ulvklo, Morgan
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology. Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Nygårds, Jonas
    Linköping University, Department of Mechanical Engineering, Fluid and Mechanical Engineering Systems. Linköping University, The Institute of Technology. Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Karlholm, Jörgen
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Skoglar, Per
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology. Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Nilsson, Jonas
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    A sensor management framework for autonomous UAV surveillance2005In: Proceedings of SPIE 5787, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications II, SPIE - International Society for Optical Engineering, 2005, p. 48-61Conference paper (Refereed)
    Abstract [en]

    This paper presents components of a sensor management architecture for autonomous UAV systems equipped with IR and video sensors, focusing on two main areas. Firstly, a framework inspired by optimal control and information theory is presented for concurrent path and sensor planning. Secondly, a method for visual landmark selection and recognition is presented. The latter is intended to be used within a SLAM (Simultaneous Localization and Mapping) architecture for visual navigation. Results are presented on both simulated and real sensor data, the latter from the MASP system (Modular Airborne Sensor Platform), an in-house developed UAV surrogate system containing a gimballed IR camera, a video sensor, and an integrated high performance navigation system.

  • 74.
    Wadströmer, Niclas
    et al.
    Swedish Defence Research Institute (FOI), Linköping, Sweden.
    Ahlberg, Jörgen
    Swedish Defence Research Institute (FOI), Linköping, Sweden.
    Svensson, Thomas
    Swedish Defence Research Institute (FOI), Linköping, Sweden.
    A new hyperspectral dataset and some challenges2010In: Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI / [ed] Sylvia S. Shen; Paul E. Lewis, SPIE - International Society for Optical Engineering, 2010, p. Art.nr. 7695-22-Conference paper (Refereed)
    Abstract [en]

    We present a new hyperspectral data set that FOI will keep publicly available. The hyperspectral data set was collected in an airborne measurement over the countryside. The spectral resolution was about 10 nm which allowed registrations in 60 spectral bands in the visual and near infrared range (390-960 nm). Objects with various signature properties were placed in three areas: the edge of a wood, an open field and a rough open terrain. Several overflights were performed over the areas. Between the overflights some of the objects were moved, representing different scenarios. Our interest is primarily in anomaly detection of man-made objects placed in nature where no such objects are expected. The objects in the trial were military and civilian vehicles, boards of different size and a camouflage net. The size of the boards range from multipixel to subpixel size. Due to wind and cloud conditions the stability and the flight height of the airplane vary between the overflights, which makes the analysis extra challenging. 

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