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  • 1.
    Andersson, Mats
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Hybrid Image Processing Architecture1988Report (Other academic)
  • 2.
    Andersson, Mats
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Implementation of Image Processing Operations from Analogue Convolver Responses1989In: Proceedings of the SSAB Conference on Image Analysis: Gothenburg, Sweden, 1989, p. 67-74Conference paper (Refereed)
  • 3.
    Andersson, Thord
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Nordberg, Klas
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    WITAS Project at Computer Vision Laboratory; A status report (Jan 1998)1998In: Proceedings of the SSAB symposium on image analysis: Uppsala, Sweden, 1998, p. 113-116Conference paper (Refereed)
    Abstract [en]

    WITAS will be engaged in goal-directed basic research in the area of intelligent autonomous vehicles and other autonomous systems. In this paper an overview of the project is given together with a presentation of our research interests in the project. The current status of our part in the project is also given.

  • 4.
    Bigun, Josef
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Central Symmetry Modelling1986Report (Other academic)
    Abstract [en]

    A definition of central symmetry for local neighborhoods of 2-D images is given. A complete ON-set of centrally symmetric basis functions is proposed. The local neighborhoods are expanded in this basis. The behavior of coefficient spectrum obtained by this expansion is proposed to be the foundation of central symmetry parameters of the neighbqrhoods. Specifically examination of two such behaviors are proposed: Point concentration and line concentration of the energy spectrum. Moreover, the study of these types of behaviors of the spectrum are shown to be possible to do in the spatial domain.

  • 5.
    Bigun, Josef
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Central Symmetry Modelling1986In: Proceedings of EUSIPCO-86, Third European Signal Processing Conference / [ed] Ian T. Young, 1986, p. 883-886Conference paper (Refereed)
    Abstract [en]

    A definition of central symmetry for local neighborhoods of 2-D images is given. A complete ON-set of centrally symmetric basis functions is proposed. The local neighborhoods are expanded in this basis. The behavior of coefficient spectrum obtained by this expansion is proposed to be the foundation of central symmetry parameters of the neighbqrhoods. Specifically examination of two such behaviors are proposed: Point concentration and line concentration of the energy spectrum. Moreover, the study of these types of behaviors of the spectrum are shown to be possible to do in the spatial domain.

  • 6.
    Bigun, Josef
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Optical Flow Based on the Inertia Matrix of the Frequency Domain1988In: Proceedings from SSAB Symposium on Picture Processing: Lund University, Sweden, 1988, p. 132-135Conference paper (Refereed)
  • 7.
    Bigun, Josef
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Optimal Orientation Detection of Linear Symmetry1987In: Proceedings of the IEEE First International Conference on Computer Vision, 1987, p. 433-438Conference paper (Refereed)
    Abstract [en]

    The problem of optimal detection of orientation in arbitrary neighborhoods is solved in the least squares sense. It is shown that this corresponds to fitting an axis in the Fourier domain of the n-dimensional neighborhood, the solution of which is a well known solution of a matrix eigenvalue problem. The eigenvalues are the variance or inertia with respect to the axes given by their respective eigen vectors. The orientation is taken as the axis given by the least eigenvalue. Moreover it is shown that the necessary computations can be pursued in the spatial domain without doing a Fourier transformation. An implementation for 2-D is presented. Two certainty measures are given corresponding to the orientation estimate. These are the relative or the absolute distances between the two eigenvalues, revealing whether the fitted axis is much better than an axis orthogonal to it. The result of the implementation is verified by experiments which confirm an accurate orientation estimation and reliable certainty measure in the presence of additive noise at high level as well as low levels.

  • 8.
    Bigun, Josef
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Multidimensional orientation estimation with applications to texture analysis and optical flow1991In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 13, no 8, p. 775-790Article in journal (Refereed)
    Abstract [en]

    The problem of detection of orientation in finite dimensional Euclidean spaces is solved in the least squares sense. In particular, the theory is developed for the case when such orientation computations are necessary at all local neighborhoods of the n-dimensional Euclidean space. Detection of orientation is shown to correspond to fitting an axis or a plane to the Fourier transform of an n-dimensional structure. The solution of this problem is related to the solution of a well-known matrix eigenvalue problem. Moreover, it is shown that the necessary computations can be performed in the spatial domain without actually doing a Fourier transformation. Along with the orientation estimate, a certainty measure, based on the error of the fit, is proposed. Two applications in image analysis are considered: texture segmentation and optical flow. An implementation for 2-D (texture features) as well as 3-D (optical flow) is presented. In the case of 2-D, the method exploits the properties of the complex number field to by-pass the eigenvalue analysis, improving the speed and the numerical stability of the method. The theory is verified by experiments which confirm accurate orientation estimates and reliable certainty measures in the presence of noise. The comparative results indicate that the proposed theory produces algorithms computing robust texture features as well as optical flow. The computations are highly parallelizable and can be used in realtime image analysis since they utilize only elementary functions in a closed form (up to dimension 4) and Cartesian separable convolutions.

  • 9.
    Bigun, Josef
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Multidimensional orientation: texture analysis and optical flow1991In: Proceedings of the SSAB Symposium on Image Analysis: Stockholm, 1991, p. 110-113Conference paper (Refereed)
  • 10.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Hierarchical Feature Extraction for Computer-Aided Analysis of Mammograms1992Report (Other academic)
  • 11.
    Bårman, Håkan
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Computer-Aided Analysis of Mammograms1993In: Proceedings Nordic symposium on PACS, Digital Radiology and Telemedicine: SPRI, Swedish Institute for Health Services Development, Stockholm, 1993, p. 76-Conference paper (Refereed)
  • 12.
    Bårman, Håkan
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Corner Detection Using Local Symmetry1988In: Proceedings from SSAB Symposium on Picture Processing: Lund University, Sweden, 1988Conference paper (Refereed)
  • 13.
    Bårman, Håkan
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Hierarchical Feature Extraction for Computer- Aided Analysis of Mammograms1993In: BIOMEDICAL IMAGE PROCESSING IV AND BIOMEDICAL VISUALIZATI0N: Part of 1993 SPIE/SPSE Symposium on Electronic Imaging January 31 - February 5, 1993, San Jose, California, USA, 1993Conference paper (Refereed)
  • 14.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Using Simple Local Fourier Domain Models for Computer-Aided Analysis of Mammograms1993In: SCIA8: Tromso, NOBIM, Norwegian Society for Image Processing and Pattern Recognition , 1993, p. 479-486Conference paper (Refereed)
  • 15.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Haglund, Leif
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Feature Extraction for Computer-Aided Analysis of Mammograms1994In: State of the Art in Digital Mammographic Image Analysis: eds K W Bowyer and S Astley / [ed] K.W. Bowyer, S. Astley, Singapore: World Scientific Publishing Co. Ltd , 1994Chapter in book (Refereed)
    Abstract [en]

    A framework for computer-aided analysis of mammograms is described. General computer vision algorithms are combined with application specific procedures in a hierarchical fashion. The system is under development and is currently limited to detection of a few types of suspicious areas. The image features are extracted by using feature extraction methods where wavelet techniques are utilized. A low-pass pyramid representation of the image is convolved with a number of quadrature filters. The filter outputs are combined according to simple local Fourier domain models into parameters describing the local neighborhood with respect to the model. This produces estimates for each pixel describing local size, orientation, Fourier phase, and shape with confidence measures associated to each parameter. Tentative object descriptions are then extracted from the pixel-based features by application-specific procedures with knowledge of relevant structures in mammograms. The orientation, relative brightness and shape of the object are obtained by selection of the pixel feature estimates which best describe the object. The list of object descriptions is examined by procedures, where each procedure corresponds to a specific type of suspicious area, e.g. clusters of microcalcifications.

  • 16.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Haglund, Leif
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Feature Extraction for Computer-Aided Analysis of Mammograms1993In: International journal of pattern recognition and artificial intelligence, ISSN 0218-0014, Vol. 7, no 6, p. 1339-1356Article in journal (Refereed)
    Abstract [en]

    A framework for computer-aided analysis of mammograms is described. General computer vision algorithms are combined with application specific procedures in a hierarchical fashion. The system is under development and is currently limited to detection of a few types of suspicious areas. The image features are extracted by using feature extraction methods where wavelet techniques are utilized. A low-pass pyramid representation of the image is convolved with a number of quadrature filters. The filter outputs are combined according to simple local Fourier domain models into parameters describing the local neighborhood with respect to the model. This produces estimates for each pixel describing local size, orientation, Fourier phase, and shape with confidence measures associated to each parameter. Tentative object descriptions are then extracted from the pixel-based features by application-specific procedures with knowledge of relevant structures in mammograms. The orientation, relative brightness and shape of the object are obtained by selection of the pixel feature estimates which best describe the object. The list of object descriptions is examined by procedures, where each procedure corresponds to a specific type of suspicious area, e.g. clusters of microcalcifications.

  • 17.
    Bårman, Håkan
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    A new approach to curvature estimation and description1989In: 3rd International Conference on Image Processing and its Applications: Warwick, Great Britain, 1989, p. 54-58Conference paper (Refereed)
  • 18.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Hierarchical Curvature Estimation and Description1990Report (Other academic)
  • 19.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Tensor Field Filtering and Curvature Estimation1990In: Proceedings of the SSAB Symposium on Image Analysis: Linköping, Sweden, 1990, p. 175-178Conference paper (Refereed)
  • 20.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Näppä, L.
    n/a.
    Context Dependent Hierarchical Image Processing for Remote Sensing Data.1986Report (Other academic)
  • 21.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Haglund, Leif
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Context Dependent Hierarchical Image Processing for Remote Sensing Data, Part Two: Contextual Classification and Segmentation1988Report (Other academic)
  • 22.
    Bårman, Håkan
    et al.
    n/a.
    Haglund, Leif
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Estimation of Velocity, Acceleration and Disparity in Time Sequences1991In: Proceedings of IEEE Workshop on Visual Motion: Princeton, NJ, USA, IEEE Computer Society Press , 1991, p. 44-51Conference paper (Refereed)
  • 23.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Filtering Strategy for Orientation and Curvature Description1989In: The 6th Scandinavian Conference on Image Analysis: Oulu, Finland, 1989, p. 886-889Conference paper (Refereed)
  • 24.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Note on Estimation of Optical Flow and Acceleration1992Report (Other academic)
  • 25.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Mechanisms for Striate Cortex Organization1989Report (Other academic)
  • 26.
    Bårman, Håkan
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Using Principal Direction Estimates for Shape and Acceleration Description1991In: Proceedings of the SSAB Symposium on Image Analysis: Stockholm, 1991Conference paper (Refereed)
  • 27.
    Bårman, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Using Principal Direction Estimates for Shape and Acceleration Description1991Report (Other academic)
  • 28.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Kuchcinski, Krzysztof
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Sandewall, Erik Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, CASL - Cognitive Autonomous Systems Laboratory.
    Nordberg, Klas
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Skarman, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, EMTEK - Entity for Methodology and Technology of Knowledge Management.
    Wiklund, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    The WITAS unmanned aerial vehicle project2000In: Proceedings of the 14th European Conference on Artificial Intelligence (ECAI) / [ed] Werner Horn, Amsterdam: IOS Press , 2000, , p. 747-755p. 747-755Conference paper (Refereed)
    Abstract [en]

    The purpose of this paper is to provide a broad overview of the WITAS Unmanned Aerial Vehicle Project. The WITAS UAV project is an ambitious, long-term basic research project with the goal of developing technologies and functionalities necessary for the successful deployment of a fully autonomous UAV operating over diverse geographical terrain containing road and traffic networks. Theproject is multi-disciplinary in nature, requiring many different research competences, and covering a broad spectrum of basic research issues, many of which relate to current topics in artificial intelligence. A number of topics considered are knowledge representation issues, active vision systems and their integration with deliberative/reactive architectures, helicopter modeling and control, ground operator dialogue systems, actual physical platforms, and a number of simulation techniques.

  • 29.
    Duits, Remco
    et al.
    Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    ter Haar Romeny, Bart M.
    Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands .
    Image Analysis and Reconstruction using a Wavelet Transform Constructed from a Reducible Representation of the Euclidean Motion Group2007In: International Journal of Computer Vision, ISSN 0920-5691, E-ISSN 1573-1405, Vol. 72, no 1, p. 79-102Article in journal (Refereed)
    Abstract [en]

    Inspired by the early visual system of many mammalians we consider the construction of-and reconstruction from- an orientation score Uf:R2×S1→C as a local orientation representation of an image, f:R2→R . The mapping f↦Uf is a wavelet transform Wψ corresponding to a reducible representation of the Euclidean motion group onto L2(R2) and oriented wavelet ψ∈L2(R2) . This wavelet transform is a special case of a recently developed generalization of the standard wavelet theory and has the practical advantage over the usual wavelet approaches in image analysis (constructed by irreducible representations of the similitude group) that it allows a stable reconstruction from one (single scale) orientation score. Since our wavelet transform is a unitary mapping with stable inverse, we directly relate operations on orientation scores to operations on images in a robust manner.

    Furthermore, by geometrical examination of the Euclidean motion group G=R2R×T , which is the domain of our orientation scores, we deduce that an operator Φ on orientation scores must be left invariant to ensure that the corresponding operator W−1ψΦWψ on images is Euclidean invariant. As an example we consider all linear second order left invariant evolutions on orientation scores corresponding to stochastic processes on G. As an application we detect elongated structures in (medical) images and automatically close the gaps between them.

    Finally, we consider robust orientation estimates by means of channel representations, where we combine robust orientation estimation and learning of wavelets resulting in an auto-associative processing of orientation features. Here linear averaging of the channel representation is equivalent to robust orientation estimation and an adaptation of the wavelet to the statistics of the considered image class leads to an auto-associative behavior of the system.

  • 30.
    Edholm, Paul
    et al.
    n/a.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Petersson, C.
    n/a.
    Ectomography: A New Radiographic Method for Reproducing a Selected Slice of Varying Thickness1980In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 21, no 4, p. 433-442Article in journal (Refereed)
    Abstract [en]

    The mathematical basis is described of a new radiographic method by which an arbitrarily thick layer of the patient may be reconstructed. The reconstruction is performed from at least 60 images of the volume under examination. Each of these images, which have to be in digital form, is subjected to a special filtration process of its spatial frequencies. The combination of all the images will form the resulting image of the layer--the ectomogram. The method has been analysed and tested in experiments simulated with a computer.

  • 31.
    Farnebäck, Gunnar
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Detection of point-shaped targets1996Report (Other academic)
    Abstract [en]

    This report documents work done at the request of the Swedish Defense Research Establishment. The studied problem is that of detecting point-shaped targets, i.e. targets whose only significant property is that of being very small, in a cluttered environment. Three approaches to the problem have been considered. The first one, based on motion compensation, was rejected at an early stage due to expected problems with robustness and computational demands. The second method, based on background modeling with principal components, turned out successful and has been studied in depth, including discussion of various extensions and improvements of the presented algorithm. Finally, a Wiener filter approach has also turned out successful, including an approximation with separable filters. The methods have been tested on sequences obtained by an IR sensor. While both the two latter approaches work well on the test sequences, the Wiener filter is simpler and computationally less expensive than the background modeling. On the other hand, the background modeling is likely to have better possibilities for extensions and improvements.

  • 32.
    Felsberg, Michael
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Forssén, Per-Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    A COSPAL Subsystem: Solving a Shape-Sorter Puzzle2005In: AAAI Fall Symposium: From Reactive to Anticipatory Cognitive Embedded Systems, FS-05-05, AAAI Press , 2005, p. 65-69Conference paper (Refereed)
    Abstract [en]

     To program a robot to solve a simple shape-sorter puzzle is trivial. To devise a Cognitive System Architecture, which allows the system to find out by itself how to go about a solution, is less than trivial. The development of such an architecture is one of the aims of the COSPAL project, leading to new techniques in vision based Artificial Cognitive Systems, which allow the development of robust systems for real dynamic environments. The systems developed under the project itself remain however in simplified scenarios, likewise the shape-sorter problem described in the present paper. The key property of the described system is its robustness. Since we apply association strategies of local features, the system behaves robustly under a wide range of distortions, as occlusion, colour and intensity changes. The segmentation step which is applied in many systems known from literature is replaced with local associations and view-based hypothesis validation. The hypotheses used in our system are based on the anticipated state of the visual percepts. This state replaces explicit modeling of shapes. The current state is chosen by a voting system and verified against the true visual percepts. The anticipated state is obtained from the association to the manipulator actions, where reinforcement learning replaces the explicit calculation of actions. These three differences to classical schemes allow the design of a much more generic and flexible system with a high level of robustness. On the technical side, the channel representation of information and associative learning in terms of the channel learning architecture are essential ingredients for the system. It is the properties of locality, smoothness, and non-negativity which make these techniques suitable for this kind of application. The paper gives brief descriptions of how different system parts have been implemented and show some examples from our tests.

  • 33.
    Felsberg, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Anisotropic Channel Filtering2003In: SCIA: Gothenburg, Sweden, 2003, p. 755-762Conference paper (Refereed)
    Abstract [en]

    Channel smoothing is an alternative to diffusion filtering for robust estimation of image features. Its main advantages are speed, stability with respect to parameter changes, and a simple implementation. However, channel smoothing becomes instable in certain situations, typically for elongated, periodic patterns like for instance fingerprints. As for the diffusion filtering an anisotropic extension is required in these cases. In this paper we introduce a new method for anisotropic channel smoothing which is comparable to coherence enhancing diffusion, but faster and easier to implement. Anisotropic channel smoothing implements an orientation adaptive non-linear filtering scheme as a special case of adaptive channel filtering. The smoothing algorithm is applied to several fingerprint images and the results are compared to those of coherence enhancing diffusion.

  • 34.
    Felsberg, Michael
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Fusing Dynamic Percepts and Symbols in Cognitive Systems2008In: International Conference on Cognitive Systems, 2008Conference paper (Refereed)
  • 35.
    Felsberg, Michael
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    P-Channels: Robust Multivariate M-Estimation of Large Datasets2006In: ICPR,2006, 2006Conference paper (Refereed)
  • 36.
    Felsberg, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    POI detection using channel clustering and the 2D energy tensor2004In: Proceedings of Pattern Recognition, 26th DAGM Symposium / [ed] Carl Edward Rasmussen, Heinrich H. Bülthoff, Bernhard Schölkopf and Martin A. Giese, SpringerLink , 2004, Vol. 3175, p. 103-110Conference paper (Refereed)
    Abstract [en]

    In this paper we address one of the standard problems of image processing and computer vision: The detection of points of interest (POI). We propose two new approaches for improving the detection results. First, we define an energy tensor which can be considered as a phase invariant extension of the structure tensor. Second, we use the channel representation for robustly clustering the POI information from the first step resulting in sub-pixel accuracy for the localisation of POI. We compare our method to several related approaches on a theoretical level and show a brief experimental comparison to the Harris detector.

  • 37.
    Felsberg, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Exploratory learning structures in artificial cognitive systems2009In: Image and Vision Computing, ISSN 0262-8856, Vol. 27, no 11, p. 1671-1687Article in journal (Refereed)
    Abstract [en]

    The major goal of the COSPAL project is to develop an artificial cognitive system architecture, with the ability to autonomously extend its capabilities. Exploratory learning is one strategy that allows an extension of competences as provided by the environment of the system. Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models. Incremental or online learning is a crucial requirement to perform exploratory learning. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning, and in this paper we focus on the organization of cognitive systems for efficient operation. Learning is used over the entire system. It is organized in the form of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail. We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user (teacher) and system is a major difference to classical robotics systems, where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems. We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.

  • 38.
    Felsberg, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Jonsson, Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Moe, Anders
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Exploratory Learning Structure in Artificial Cognitive Systems2006Report (Other academic)
    Abstract [en]

    One major goal of the COSPAL project is to develop an artificial cognitive system architecture with the capability of exploratory learning. Exploratory learning is a strategy that allows to apply generalization on a conceptual level, resulting in an extension of competences. Whereas classical learning methods aim at best possible generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class. Incremental or online learning is an inherent requirement to perform exploratory learning.

    Exploratory learning requires new theoretic tools and new algorithms. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning and in this paper we focus on its algorithmic aspect. Learning is performed in terms of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail.

    We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user (’teacher’) and system is a major difference to most existing systems where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems.

    We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module.We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.

  • 39.
    Felsberg, Michael
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Wiklund, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Jonsson, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Moe, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Exploratory Learning Strucutre in Artificial Cognitive Systems2007In: International Cognitive Vision Workshop, Bielefeld: eCollections , 2007Conference paper (Other academic)
    Abstract [en]

    One major goal of the COSPAL project is to develop an artificial cognitive system architecture with the capability of exploratory learning. Exploratory learning is a strategy that allows to apply generalization on a conceptual level, resulting in an extension of competences. Whereas classical learning methods aim at best possible generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class. Incremental or online learning is an inherent requirement to perform exploratory learning.

    Exploratory learning requires new theoretic tools and new algorithms. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning and in this paper we focus on its algorithmic aspect. Learning is performed in terms of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail.

    We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user ('teacher') and system is a major difference to most existing systems where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems.

    We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.

  • 40.
    Forssen, Per-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Channel Representation of Colour Images2002Report (Other academic)
    Abstract [en]

    In this report we describe how an RGB component colour image may be expanded into a set of channel images, and how the original colour image may be reconstructed from these. We also demonstrate the effect of averaging on the channel images and how it differs from conventional averaging. Finally we demonstrate how boundaries can be detected as a change in the confidence of colour state.

  • 41.
    Forssén, Per-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Robust multi-scale extraction of blob features2003In: Proceedings or the 13th Scandinavian Conference, SCIA 2003 / [ed] Josef Bigun and Tomas Gustavsson, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2003, Vol. 2749, p. 769-769Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for detection of homogeneous regions in grey-scale images, representing them as blobs. In order to be fast, and not to favour one scale over others, the method uses a scale pyramid. In contrast to most multi-scale methods this one is non-linear, since it employs robust estimation rather than averaging to move through scale-space. This has the advantage that adjacent and partially overlapping clusters only affect each other's shape, not each other's values. It even allows blobs within blobs, to provide a pyramid blob structure of the image.

  • 42.
    Forssén, Per-Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Sparse feature maps in a scale hierarchy2000In: Algebraic Frames for the Perception-Action Cycle, Proceedings Second International Workshop, AFPAC 2000 / [ed] Gerald Sommer and Yehoshua Y. Zeevi, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2000, Vol. 1888, p. 186-196Conference paper (Refereed)
    Abstract [en]

    This article describes an essential step towards what is called a view centered representation of the low-level structure in an image. Instead of representing low-level structure (lines and edges) in one compact feature map, we will separate structural information into several feature maps, each signifying features at a characteristic phase, in a specific scale. By characteristic phase we mean the phases 0, pi, and +/-pi/2, corresponding to bright, and dark lines, and edges between different intensity levels, or colours. A lateral inhibition mechanism selects the strongest feature within each local region of scale represented. The scale representation is limited to maps one octave apart, but can be interpolated to provide a continous representation. The resultant image representation is sparse, and thus well suited for further processing, such as pattern detection.

  • 43.
    Forssén, Per-Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Channel Associative Networks for Multiple Valued Mappings2006In: 2nd International Cognitive Vision Workshop, 2006, p. 4-11Conference paper (Other academic)
  • 44.
    Forssén, Per-Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Johansson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Learning under Perceptual Aliasing2005Report (Other academic)
  • 45.
    Granlund, Gösta
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    A Cognitive Vision Architecture Integrating Neural Networks with Symbolic Processing2006In: Künstliche Intelligenz, ISSN 0933-1875, no 2, p. 18-24Article in journal (Other academic)
    Abstract [en]

    A fundamental property of cognitive vision systems is that they shall be extendable, which requires that they can both acquire and store information autonomously. The paper discusses organization of systems to allow this, and proposes an architecture for cognitive vision systems. The architecture consists of two parts. The first part, step by step learns a mapping from percepts directly onto actions or states. In the learning phase, action precedes perception, as action space is much less complex. This requires a semantic information representation, allowing computation and storage with respect to similarity. The second part uses invariant or symbolic representations, which are derived mainly from system and action states. Through active exploration, a system builds up concept spaces or models. This allows the system to subsequently acquire information using passive observation or language. The structure has been used to learn object properties, and constitutes the basic concepts for a European project COSPAL, within the IST programme.

  • 46.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Biological vision: a source of challenges and ideas1995In: DSAGM, Dansk Selskab for Genkendelse af Mønstre: Copenhagen, Denmark, 1995Conference paper (Other academic)
  • 47.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Description of texture using the general operator approch1980In: 5th International Conference on Pattern Recognition, 1980, p. 776-779Conference paper (Refereed)
  • 48.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Does Vision Inevitably Have to be Active?1999In: Proceedings of the 11th Scandinavian Conference on Image Analysis: Kangerlussuaq, Greenland, 1999Conference paper (Refereed)
    Abstract [en]

    There is no indication that it will ever be possible to find some simple trick that miraculously solves most problems in vision. It turns out that the processing system must be able to implement a model structure, the complexity of which is directly related to the structural complexity of the problem under consideration in the external world. It has become increasingly apparent that Vision cannot be treated in isolation from the response generation, because a very high degree of integration is required between different levels of percepts and corresponding response primitives. The response to be produced at a given instance is as much dependent upon the state of the system, as the percepts impinging upon the system. In addition, it has become apparent that many classical aspects of perception, such as geometry, probably do not belong to the percept domain of a Vision system, but to the response domain. This article will focus on what are considered crucial problems in Vision for robotics for the future, rather than on the classical solutions today. It will discuss hierarchical architectures for combination of percept and response primitives. It will discuss the concept of combined percept–response invariances as important structural elements for Vision. It will be maintained that learning is essential to obtain the necessary flexibility and adaptivity. In consequence, it will be argued that invariances for the purpose of Vision are not abstractly geometrical, but derived from the percept–response interaction with the environment. The issue of information representation becomes extremely important in distributed structures of the types foreseen, where uncertainty of information has to be stated for update of models and associated data. The question of object representation is central to the paper. Equivalence is established between the representations of response, geometry and time. Finally an integrated percept–response structure is proposed for flexible response control.

  • 49.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Does Vision Inevitably Have to be Active?1998Report (Other academic)
  • 50.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    ESPRIT Project BRA 3038: Vision as Process, Final Report1993Report (Other academic)
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