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  • 51.
    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.

  • 52.
    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.

  • 53.
    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.

  • 54.
    Markus, Nenad
    et al.
    University of Zagreb.
    Pandzic, Igor
    University of Zagreb.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion2017Conference 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.

  • 55.
    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.

  • 56.
    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.

  • 57.
    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.

  • 58.
    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.

  • 59.
    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.

  • 60.
    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.

  • 61.
    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.

  • 62.
    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.

  • 63.
    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.

  • 64.
    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.  

  • 65.
    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. 

  • 66.
    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.

  • 67.
    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.

  • 68.
    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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