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  • 201.
    Knutsson, Hans
    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.
    Texture Analysis Using Two-Dimensional Quadrature Filters1983In: IEEE Computer Society Workshop on Computer Architecture for Pattern Analysis and Image Database Management - CAPAIDM: Pasadena, 1983Conference paper (Refereed)
  • 202.
    Knutsson, Hans
    et al.
    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.
    Bårman, Håkan
    n/a.
    A Note on Estimation of 4D Orientation1990In: Proceedings of the SSAB Symposium on Image Analysis: Linköping, 1990, p. 192-195Conference paper (Refereed)
  • 203.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Haglund, Leif
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Adaptive Filtering1995In: Signal Processing for Computer Vision / [ed] Gösta H. Grandlund and Hans Knutsson, Dordrecht: Kluwer , 1995, Vol. 2749, p. 309-342Chapter in book (Refereed)
    Abstract [en]

    This chapter presents a computationally efficient technique for adaptive filtering of n-dimensional  signals.

    The approach is based on the local signal description given by the orientation tensor discussed in Chapter 6. The adaptive filter output is synthesized as a tensor-controlled weighted summation of shift-invariant filter outputs. Several examples of adaptive filtering in two and three dimensions are given. The chapter contains original results on the extension of the techniques to n dimensions

  • 204.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Haglund, Leif
    n/a.
    Bårman, Håkan
    n/a.
    A Tensor Based Approach to Structure Analysis and Enhancement in 2D, 3D and 4D1991In: Workshop Program, Seventh Workshop on Multidimentional Signal Processing: Lake Placid, New York, USA, IEEE Signal Processing Society , 1991Conference paper (Refereed)
  • 205.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Haglund, Leif
    n/a.
    Bårman, Håkan
    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 Framework for Anisotropic Adaptive Filtering and Analysis of Image Sequences and Volumes1992In: Proceedings ICASSP-92: San Fransisco, CA, USA, IEEE , 1992Conference paper (Refereed)
  • 206.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Haglund, Leif
    n/a.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Adaptive Filtering of Image Sequences and Volumes1992In: Proceedings of International Conference on Automation, Robotics and Computer Vision: Singapore, 1992Conference paper (Refereed)
  • 207.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Haglund, Leif
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    A New Approach to Image Enhancement Using Tensor Fields1990In: Proceedings of the PROART Workshop on Vision: Sophia Antipolis, France, 1990, p. 111-115Conference paper (Refereed)
  • 208.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Haglund, Leif
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Tensor Field Controlled Image Sequence Enhancement1990In: Proceedings of the SSAB Symposium on Image Analysis: Linköping, Sweden, 1990, p. 163-167Conference paper (Refereed)
  • 209.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Herberthson, Magnus
    Linköping University, Department of Mathematics, Mathematics and Applied Mathematics. Linköping University, Faculty of Science & Engineering.
    Westin, Carl-Fredrik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    An Iterated Complex Matrix Approach for Simulation and Analysis of Diffusion MRI Processes2015In: MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2015, PT I, SPRINGER INT PUBLISHING AG , 2015, Vol. 9349, p. 61-68Conference paper (Refereed)
    Abstract [en]

    We present a novel approach to investigate the properties of diffusion weighted magnetic resonance imaging (dMRI). The process of restricted diffusion of spin particles in the presence of a magnetic field is simulated by an iterated complex matrix multiplication approach. The approach is based on first principles and provides a flexible, transparent and fast simulation tool. The experiments carried out reveals fundamental features of the dMRI process. A particularly interesting observation is that the induced speed of the local spatial spin angle rate of change is highly shift variant. Hence, the encoding basis functions are not the complex exponentials associated with the Fourier transform as commonly assumed. Thus, reconstructing the signal using the inverse Fourier transform leads to large compartment estimation errors, which is demonstrated in a number of 1D and 2D examples. In accordance with previous investigations the compartment size is under-estimated. More interestingly, however, we show that the estimated shape is likely to be far from the true shape using state of the art clinical MRI scanners.

  • 210.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Post, B. von
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Optimization of Arithmetic Neighborhood Operations for Image Processing1980In: Proceedings of the First Scandinavian Conference on Image Analysis: Linköping, Sweden, 1980Conference paper (Refereed)
  • 211.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl-Fredrik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Harvard Medical School, Laboratory of Mathematics in Imaging (LMI).
    An Information Theoretic Approach to Optimal Q-space Sampling2014In: ISMRM-ESMRMB 2014, 2014Conference paper (Other academic)
  • 212.
    Knutsson, Hans
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Harvard School of Medicin.
    Charged Containers for Optimal 3D Q-space Sampling2013In: Proceedings of the International Society for Magnetic Resonance in Medicine annual meeting (ISMRM'13), International Society for Magnetic Resonance in Medicine ( ISMRM ) , 2013Conference paper (Other academic)
    Abstract [en]

    Conclusions: We have presented a novel method for generating evenly distributed samples in a part of q-space that can be pre- specified in a general way. We have demonstrated the feasibility for two shapes, a sphere and a cube. The results are interesting from several points of view. There is a market tendency for the samples to group in shells indicating that the present work may provide a preferable alternative to recently proposed shell-interaction schemes [9]. The distributions attained for the cube case are far from Cartesian, this may be an advantage in a sparse reconstruction, e.g. compressed sensing, setting.

  • 213.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl-Fredrik
    Harvard Medical School, USA .
    From Expected Propagator Distribution to Optimal Q-space Sample Metric2014In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III / [ed] Polina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe, Springer, 2014, p. 217-224Conference paper (Refereed)
    Abstract [en]

    We present a novel approach to determine a local q-space metric that is optimal from an information theoretic perspective with respect to the expected signal statistics. It should be noted that the approach does not attempt to optimize the quality of a pre-defined mathematical representation, the estimator. In contrast, our suggestion aims at obtaining the maximum amount of information without enforcing a particular feature representation.

    Results for three significantly different average propagator distributions are presented. The results show that the optimal q-space metric has a strong dependence on the assumed distribution in the targeted tissue. In many practical cases educated guesses can be made regarding the average propagator distribution present. In such cases the presented analysis can produce a metric that is optimal with respect to this distribution. The metric will be different at different q-space locations and is defined by the amount of additional information that is obtained when adding a second sample at a given offset from a first sample. The intention is to use the obtained metric as a guide for the generation of specific efficient q-space sample distributions for the targeted tissue.

  • 214.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Local Frequency1995In: Signal Processing for Computer Vision / [ed] Gösta H. Grandlund and Hans Knutsson, Dordrecht: Kluwer , 1995, Vol. 2749, p. 279-295Chapter in book (Refereed)
    Abstract [en]

    This chapter deals with the estimation of local frequency and bandwidth. Local frequency is an important concept which provides an indication of the appropriate range of scales for subsequent analysis. A number of one-dimensional and two-dimensional examples of local frequency and bandwidth estimation are given.

  • 215.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl-Fredrik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
    Monomial Phase: A Matrix Representation of Local Phase2014In: Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data / [ed] Carl-Fredrik Westin, Anna Vilanova, Bernhard Burgeth, Springer, 2014, p. 37-73Chapter in book (Other academic)
    Abstract [en]

    Local phase is a powerful concept which has been successfully used in many image processing applications. For multidimensional signals the concept of phase is complex and there is no consensus on the precise meaning of phase. It is, however, accepted by all that a measure of phase implicitly carries a directional reference. We present a novel matrix representation of multidimensional phase that has a number of advantages. In contrast to previously suggested phase representations it is shown to be globally isometric for the simple signal class. The proposed phase estimation approach uses spherically separable monomial filter of orders 0, 1 and 2 which extends naturally to N dimensions. For 2-dimensional simple signals the representation has the topology of a Klein bottle. For 1-dimensional signals the new phase representation reduces to the original definition of amplitude and phase for analytic signals. Traditional phase estimation using quadrature filter pairs is based on the analytic signal concept and requires a pre-defined filter direction. The new monomial local phase representation removes this requirement by implicitly incorporating local orientation. We continue to define a phase matrix product which retains the structure of the phase matrix representation. The conjugate product gives a phase difference matrix in a manner similar to the complex conjugate product of complex numbers. Two motion estimation examples are given to demonstrate the advantages of this approach.

  • 216.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    n/a.
    Normalized and Differential Convolution: Methods for Interpolation and Filtering of Incomplete and Uncertain Data1993In: CVPR: New York City, USA, IEEE , 1993, p. 515-523Conference paper (Refereed)
  • 217.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    n/a.
    Normalized Convolution: Technique for Filtering Incomplete and Uncertain Data1993Conference paper (Refereed)
  • 218.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    n/a.
    Robust Estimation from Sparse Feature Fields1993In: Proceedings of EC--US Workshop: Amherst, USA, 1993Conference paper (Refereed)
  • 219.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Harvard School of Medicin.
    Tensor Metrics and Charged Containers for 3D Q-space Sample Distribution2013In: Medical Image Computing and Computer-Assisted Intervention – MICCAI / [ed] Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N., Springer, 2013, p. 679-686Conference paper (Refereed)
    Abstract [en]

    This paper extends Jones’ popular electrostatic repulsion based algorithm for distribution of single-shell Q-space samples in two fundamental ways. The first alleviates the single-shell requirement en- abling full Q-space sampling. Such an extension is not immediately ob- vious since it requires distributing samples evenly in 3 dimensions. The extension is as elegant as it is simple: Add a container volume of the de- sired shape having a constant charge density and a total charge equal to the negative of the sum of the moving point charges. Results for spherical and cubic charge containers are given. The second extension concerns the way distances between sample point are measured. The Q-space samples represent orientation, rather than direction and it would seem appropri- ate to use a metric that reflects this fact, e.g. a tensor metric. To this end we present a means to employ a generalized metric in the optimization. Minimizing the energy will result in a 3-dimensional distribution of point charges that is uniform in the terms of the specified metric. The radi- cally different distributions generated using different metrics pinpoints a fundamental question: Is there an inherent optimal metric for Q-space sampling? Our work provides a versatile tool to explore the role of differ- ent metrics and we believe it will be an important contribution to further the continuing debate and research on the matter.

  • 220.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl-Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Representing local structure using tensors II2011In: Proceedings of the 17th Scandinavian conference on Image analysis / [ed] Anders Heyden, Fredrik Kahl, Springer, 2011, p. 545-556Conference paper (Refereed)
    Abstract [en]

    Estimation of local spatial structure has a long history and numerous analysis tools have been developed. A concept that is widely recognized as fundamental in the analysis is the structure tensor. However, precisely what it is taken to mean varies within the research community. We present a new method for structure tensor estimation which is a generalization of many of it's predecessors. The method uses filter sets having Fourier directional responses being monomials of the normalized frequency vector, one odd order sub-set and one even order sub-set. It is shown that such filter sets allow for a particularly simple way of attaining phase invariant, positive semi-definite, local structure tensor estimates. We continue to compare a number of known structure tensor algorithms by formulating them in monomial filter set terms. In conclusion we show how higher order tensors can be estimated using a generalization of the same simple formulation.

  • 221.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Structure Tensor Estimation: Introducing Monomial Quadrature Filter Sets2012In: New Developments in the Visualization and Processing of Tensor Fields / [ed] David H. Laidlaw, Anna Vilanova, Springer, 2012, p. 3-28Chapter in book (Other academic)
    Abstract [en]

       "Bringing together key researchers in disciplines ranging from visualization and image processing to applications in structural mechanics, fluid dynamics, elastography, and numerical mathematics, the workshop that generated this edited volume was the third in the successful Dagstuhl series. Its aim, reflected in the quality and relevance of the papers presented, was to foster collaboration and fresh lines of inquiry in the analysis and visualization of tensor fields, which offer a concise model for numerous physical phenomena. Despite their utility, there remains a dearth of methods for studying all but the simplest ones, a shortage the workshops aim to address. Documenting the latest progress and open research questions in tensor field analysis, the chapters reflect the excitement and inspiration generated  by this latest Dagstuhl workshop, held in July 2009. The topics they address range from applications of the analysis of tensor fields to purer research into their mathematical and analytical properties. They show how cooperation and the sharing of ideas and data between those engaged in pure and applied research can open new vistas in the study of tensor fields."--Publisher's website.

  • 222.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    n/a.
    Granlund, Gösta H.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Local Multiscale Frequency and Bandwidth Estimation1994In: ICIP: Austin, Texas, 1994, p. 36-40Conference paper (Refereed)
  • 223.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    n/a.
    Westelius, Carl-Johan
    n/a.
    Filtering of Uncertain Irregularly Sampled Multidimensional Data1993In: Twenty-seventh Asilomar Conf. on Signals, Systems & Computers: Pacific Grove, California, USA, 1993, p. 1301-1309Conference paper (Refereed)
  • 224.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wilson, Roland
    Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering. University of Aston, Birmingham B4 7PB, U.K..
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Anisotropic Filtering Controlled by Image Content1981In: Proceedings of the 2nd Scandinavian Conference on Image Analysis: Finland, 1981, p. 146-151Conference paper (Refereed)
    Abstract [en]

    The related problems of enhancing and restoring noisy images have received a considerable amount of attention in recent years. Restoration methods have generally been based on minimum mean-squared error operations, such as Wiener filtering or recursive filtering. The rather vague title of enhancement has been given to a wide variety of more or less ad-hoc methods, such as median filtering, which have nonetheless been found useful. In mast cases, however, the aim is the same: an improvement of the subjective quality of the image.

  • 225.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wilson, Roland
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Anisotropic Filtering Operations for Image Enhancement and their Relation to the Visual System1981In: Proceedings of IEEE Computer Society Conference on Pattern Recognition and Image Processing: Dallas, Texas, 1981Conference paper (Refereed)
  • 226.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wilson, Roland
    University of Aston.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Anisotropic Non-Stationary Image Estimation and its Applications: Part I. Restoration of Noisy Images1983In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. COM--31, no 3, p. 388-397Article in journal (Refereed)
    Abstract [en]

    A new form of image estimator, which takes account of linear features, is derived using a signal equivalent formulation. The estimator is shown to be a nonstationary linear combination of three stationary estimators. The relation of the estimator to human visual physiology is discussed. A method for estimating the nonstationary control information is described and shown to be effective when the estimation is made from noisy data. A suboptimal approach which is computationally less demanding is presented and used in the restoration of a variety of images corrupted by additive white noise. The results show that the method can improve the quality of noisy images even when the signal-to-noise ratio is very low.

  • 227.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wilson, Roland
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Content-Dependent Anisotropic Filtering of Images1981In: Proceedings of International Conference on Digital Signal Processing: Florence, Italy, 1981Conference paper (Refereed)
  • 228.
    Landelius, Tomas
    et al.
    n/a.
    Borga, Magnus
    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.
    Reinforcement Learning Trees1996Report (Other academic)
    Abstract [en]

    Two new reinforcement learning algorithms are presented. Both use a binary tree to store simple local models in the leaf nodes and coarser global models towards the root. It is demonstrated that a meaningful partitioning into local models can only be accomplished in a fused space consisting of both input and output. The first algorithm uses a batch like statistic procedure to estimate the reward functions in the fused space. The second one uses channel coding to represent the output- and input vectors allowing a simple iterative algorithm based on competing subsystems. The behaviors of both algorithms are illustrated in a preliminary experiment.

  • 229.
    Landelius, Tomas
    et al.
    n/a.
    Haglund, Leif
    n/a.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Depth and Velocity from Orientation Tensor Fields1993In: SCIA8: Tromso, Norway, 1993Conference paper (Refereed)
    Abstract [en]

    This paper presents an algorithm for retrieving depth and velocity by estimating the 3D-orientation in an image sequence under the assumption of pure translation of the camera in a static scene. Quantitative error measurements are presented comparing the proposed algorithm to a gradient based optical flow algorithm.

  • 230.
    Landelius, Tomas
    et al.
    n/a.
    Haglund, Leif
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Depth and Velocity from Orientation Tensor Fields1993In: Proceedings of the SSAB Symposium on Image Analysis: Gothenburg, 1993Conference paper (Refereed)
  • 231.
    Landelius, Tomas
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Dynamic Tree Structure for Incremental Reinforcement Learning of Good Behavior1994Report (Other academic)
    Abstract [en]

    This paper addresses the idea of learning by reinforcement, within the theory of behaviorism. The reason for this choice is its generality and especially that the reinforcement learning paradigm allows systems to be designed, which can improve their behavior beyond that of their teacher. The role of the teacher is to define the reinforcement function, which acts as a description of the problem the machine is to solve. Gained knowledge is represented by a behavior probability density function which is approximated with a number of normal distributions, stored in the nodes of a binary tree. It is argued that a meaningful partitioning into local models can only be accomplished in a fused space consisting of both stimuli and responses. Given a stimulus, the system searches for responses likely to result in highly reinforced decisions by treating the sum of the two normal distributions on each level in the tree as a distribution describing the system's behavior at that resolution. The resolution of the response, as well as the tree growing and pruning processes, are controlled by a random variable based on the difference in performance between two consecutive levels in the tree. This results in a system that will never be content but will indefinitely continue to search for better solutions.

  • 232.
    Landelius, Tomas
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Behaviorism and Reinforcement Learning1995In: Proceedings, 2nd Swedish Conference on Connectionism: Skövde, 1995, p. 259-270Conference paper (Refereed)
  • 233.
    Landelius, Tomas
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Greedy adaptive critics for LPQ [dvs LQR] problems: Convergence Proofs1996Report (Other academic)
    Abstract [en]

    A number of success stories have been told where reinforcement learning has been applied to problems in continuous state spaces using neural nets or other sorts of function approximators in the adaptive critics. However, the theoretical understanding of why and when these algorithms work is inadequate. This is clearly exemplified by the lack of convergence results for a number of important situations. To our knowledge only two such results been presented for systems in the continuous state space domain. The first is due to Werbos and is concerned with linear function approximation and heuristic dynamic programming. Here no optimal strategy can be found why the result is of limited importance. The second result is due to Bradtke and deals with linear quadratic systems and quadratic function approximators. Bradtke's proof is limited to ADHDP and policy iteration techniques where the optimal solution is found by a number of successive approximations. This paper deals with greedy techniques, where the optimal solution is directly aimed for. Convergence proofs for a number of adaptive critics, HDP, DHP, ADHDP and ADDHP, are presented. Optimal controllers for linear quadratic regulation (LQR) systems can be found by standard techniques from control theory but the assumptions made in control theory can be weakened if adaptive critic techniques are employed. The main point of this paper is, however, not to emphasize the differences but to highlight the similarities and by so doing contribute to a theoretical understanding of adaptive critics.

  • 234.
    Landelius, Tomas
    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.
    Reinforcement Learning Adaptive Control and Explicit Criterion Maximization1996Report (Other academic)
    Abstract [en]

    This paper reviews an existing algorithm for adaptive control based on explicit criterion maximization (ECM) and presents an extended version suited for reinforcement learning tasks. Furthermore, assumptions under which the algorithm convergences to a local maxima of a long term utility function are given. Such convergence theorems are very rare for reinforcement learning algorithms working with continuous state and action spaces. A number of similar algorithms, previously suggested to the reinforcement learning community, are briefly surveyed in order to give the presented algorithm a place in the field. The relations between the different algorithms is exemplified by checking their consistency on a simple problem of linear quadratic regulation (LQR).

  • 235.
    Landelius, Tomas
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    The Learning Tree, A New Concept in Learning1993In: Proceedings of the 2nd International Conference on Adaptive and Learning Systems, 1993Conference paper (Refereed)
    Abstract [en]

    In this paper learning is considered to be the bootstrapping procedure where fragmented past experience of what to do when performing well is used for generation of new responses adding more information to the system about the environment. The gained knowledge is represented by a behavior probability density function which is decomposed into a number of normal distributions using a binary tree. This tree structure is built by storing highly reinforced stimuli-response combinations, decisions, and calculating their mean decision vector and covariance matrix. Thereafter the decision space is divided, through the mean vector, into two halves along the direction of maximal data variation. The mean vector and the covariance matrix are stored in the tree node and the procedure is repeated recursively for each of the two halves of the decision space forming a binary tree with mean vectors and covariance matrices in its nodes. The tree is the systems guide to response generation. Given a stimuli the system searches for decisions likely to give a high reinforcement. This is accomplished by treating the sum of the normal distributions in the leaves, using their mean vectors and covariance matrices as the distribution parameters, as a distribution describing the systems behavior. A response is generated by fixating the stimuli in this sum of normal distribution and use the resulting distribution, which turns out to be a new sum of normal distributions, for random generation of the response. This procedure will also make it possible for the system to have several equally plausible response to one stimuli when this is appropriate. Not applying maximum likelihood principles will lead to a more explorative system behavior avoiding local minima traps.

  • 236.
    Landelius, Tomas
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    On-Line Singular Value Decomposition of Stochastic Process Covariances1995Report (Other academic)
    Abstract [en]

    This paper presents novel algorithms for finding the singular value decomposition (SVD) of a general covariance matrix by stochastic approximation. General in the sense that also non-square, between sets, covariance matrices are dealt with. For one of the algorithms, convergence is shown using results from stochastic approximation theory. Proofs of this sort, establishing both the point of equilibrium and its domain of attraction, have been reported very rarely for stochastic, iterative feature extraction algorithms.

  • 237.
    Langer, Max
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Svensson, Björn
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Brun, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Design of fast multidimensional filters using genetic algorithms2005In: Applications of Evolutionary Computing: EvoWorkkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC Lausanne, Switzerland, March 30 - April 1, 2005 Proceedings, Springer Berlin/Heidelberg, 2005, p. 366-375Conference paper (Refereed)
    Abstract [en]

    A method for designing fast multidimensional filters using genetic algorithms is described. The filter is decomposed into component filters where coefficients can be sparsely scattered using filter networks. Placement of coefficients in the filters is done by genetic algorithms and the resulting filters are optimized using an alternating least squares approach. The method is tested on a 2-D quadrature filter and the method yields a higher quality filter in terms of weighted distortion compared to other efficient implementations that require the same ammount of computations to apply. The resulting filter also yields lower weighted distortion than the full implementation.

  • 238.
    Lindholm, Stefan
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Forsberg, Daniel
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Towards Clinical Deployment of Automated Anatomical Regions-Of-Interest2014In: Eurographics Workshop on Visual Computing for Biology and Medicine / [ed] Ivan Viola and Katja Buehler and Timo Ropinski, Eurographics - European Association for Computer Graphics, 2014, p. 137-143Conference paper (Refereed)
    Abstract [en]

    The purpose of this work is to investigate, and improve, the feasibility of advanced Region Of Interest (ROI) selection schemes in clinical volume rendering. In particular, this work implements and evaluates an Automated Anatomical ROI (AA-ROI) approach based on the combination of automatic image registration (AIR) and Distance-Based Transfer Functions (DBTFs), designed for automatic selection of complex anatomical shapes without relying on prohibitive amounts of interaction. Domain knowledge and clinical experience has been included in the project through participation of practicing radiologists in all phases of the project. This has resulted in a set of requirements that are critical for Direct Volume Rendering applications to be utilized in clinical practice and a prototype AA-ROI implementation that was developed to addresses critical points in existing solutions. The feasibility of the developed approach was assessed through a study where five radiologists investigated three medical data sets with complex ROIs, using both traditional tools and the developed prototype software. Our analysis indicate that advanced, registration based ROI schemes could increase clinical efficiency in time-critical settings for cases with complex ROIs, but also that their clinical feasibility is conditional with respect to the radiologists trust in the registration process and its application to the data.

  • 239.
    Lindholm, Stefan
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Jönsson, Daniel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Towards Data Centric Sampling for Volume Rendering2013In: SIGRAD 2013 / [ed] T. Ropinski and J. Unger, Linköping University Electronic Press , 2013, p. 55-60Conference paper (Refereed)
    Abstract [en]

    We present a new method for sampling the volume rendering integral in volume raycasting where samples are correlated based on transfer function content and data set values. This has two major advantages. First, visual artifacts stemming from structured noise, such as wood grain, can be reduced. Second, we will show that the volume data does not longer need to be available during the rendering phase; a surface representation is used instead, which opens up ample oppurtinities for rendering of large data. We will show that the proposed sampling method gives higher quality renderings with fewer samples when compared to regular sampling in the spatial domain.

  • 240.
    Lundström, C.
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Knutsson, H.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Automated Histogram Characterization of Data Sets for Image Visualization Using Alpha-Histograms2009Patent (Other (popular science, discussion, etc.))
  • 241.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    The alpha-histogram: Using Spatial Coherence to Enhance Histograms and Transfer Function Design2006In: Proceedings Eurographics/IEEE Symposium on Visualization 2006, Lisbon, Portugal, 2006, p. 227-234Conference paper (Other academic)
    Abstract [en]

    The high complexity of Transfer Function (TF) design is a major obstacle to widespread routine use of Direct Volume Rendering, particularly in the case of medical imaging. Both manual and automatic TF design schemes would benefit greatly from a fast and simple method for detection of tissue value ranges. To this end, we introduce the a-histogram, an enhancement that amplifies ranges corresponding to spatially coherent materials. The properties of the a-histogram have been explored for synthetic data sets and then successfully used to detect vessels in 20 Magnetic Resonance angiographies, proving the potential of this approach as a fast and simple technique for histogram enhancement in general and for TF construction in particular.

  • 242.
    Läthén, Gunnar
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cros, Olivier
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Non-ring Filters for Robust Detection of Linear Structures2010In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 233-236Conference paper (Refereed)
    Abstract [en]

    Many applications in image analysis include the problem of linear structure detection, e.g. segmentation of blood vessels in medical images, roads in satellite images, etc. A simple and efficient solution is to apply linear filters tuned to the structures of interest and extract line and edge positions from the filter output. However, if the filter is not carefully designed, artifacts such as ringing can distort the results and hinder a robust detection. In this paper, we study the ringing effects using a common Gabor filter for linear structure detection, and suggest a method for generating non-ring filters in 2D and 3D. The benefits of the non-ring design are motivated by results on both synthetic and natural images.

  • 243. MacLeish, P. R.
    et al.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Stern, J. H.
    n.
    The Control of the Rod Outer Segment Conductance by Cyclic-GMP and Divalent Cations.1986In: Photobiochemistry and Photobiophysics, ISSN 0165-8646, Vol. 13, p. 359-372Article in journal (Refereed)
  • 244.
    Nguyen, Tan Khoa
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Hernell, Frida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Forsell, Camilla
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Concurrent Volume Visualization of Real-Time fMRI2010In: Proceedings of the 8th IEEE/EG International Symposium on Volume Graphics / [ed] Ruediger Westermann and Gordon Kindlmann, Goslar, Germany: Eurographics - European Association for Computer Graphics, 2010, p. 53-60Conference paper (Refereed)
    Abstract [en]

    We present a novel approach to interactive and concurrent volume visualization of functional Magnetic Resonance Imaging (fMRI). While the patient is in the scanner, data is extracted in real-time using state-of-the-art signal processing techniques. The fMRI signal is treated as light emission when rendering a patient-specific high resolution reference MRI volume, obtained at the beginning of the experiment. As a result, the brain glows and emits light from active regions. The low resolution fMRI signal is thus effectively fused with the reference brain with the current transfer function settings yielding an effective focus and context visualization. The delay from a change in the fMRI signal to the visualization is approximately 2 seconds. The advantage of our method over standard 2D slice based methods is shown in a user study. We demonstrate our technique through experiments providing interactive visualization to the fMRI operator and also to the test subject in the scanner through a head mounted display.

  • 245.
    Nordberg, Klas
    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.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Representation and learning of invariance1994In: Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference, 1994, p. 585-589Conference paper (Refereed)
  • 246.
    Nordberg, Klas
    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.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Representation and Learning of Invariance1994Report (Other academic)
    Abstract [en]

    A robust, fast and general method for estimation of object properties is proposed. It is based on a representation of theses properties in terms of channels. Each channel represents a particular value of a property, resembling the activity of biological neurons. Furthermore, each processing unit, corresponding to an artificial neuron, is a linear perceptron which operates on outer products of input data. This implies a more complex space of invariances than in the case of first order characteristic without abandoning linear theory. In general, the specific function of each processing unit has to to be learned and a fast and simple learning rule is presented. The channel representation, the processing structure and the learning rule has been tested on stereo image data showing a cube with various 3D positions and orientations. The system was able to learn a channel representation for the horizontal position, the depth, and the orientation of the cube, each property invariant to the other two.

  • 247.
    Nordberg, Klas
    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.
    Some New Ideas in Signal Representation1992In: Proceedings of ECCV--92, Springer--Verlag , 1992Conference paper (Refereed)
  • 248.
    Nordberg, Klas
    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.
    Some new ideas in Signal Representation1991In: Proceedings of the SSAB Symposium on Image Analysis: Stockholm, 1991Conference paper (Refereed)
  • 249.
    Nordberg, Klas
    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.
    Local Curvature from Gradients of the Orientation Tensor Field1995Report (Other academic)
    Abstract [en]

    This paper presents an algorithm for estimation of local curvature from gradients of a tensor field that represents local orientation. The algorithm is based on an operator representation of the orientation tensor, which means that change of local orientation corresponds to a rotation of the eigenvectors of the tensor. The resulting curvature descriptor is a vector that points in the direction of the image in which the local orientation rotates anti-clockwise and the norm of the vector is the inverse of the radius of curvature. Two coefficients are defined that relate the change of local orientation with either curves or radial patterns.

  • 250.
    Nordberg, Klas
    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.
    On the Equivariance of the Orientation and the Tensor Field Representation1993In: SCIA8: Tromso, NOBIM, Norwegian Society for Image Processing and Pattern Recognition , 1993, p. 57-63Conference paper (Refereed)
    Abstract [en]

    The tensor representation has proven a successful tool as a mean to describe local multi-dimensional orientation. In this respect, the tensor representation is a map from the local orientation to a second order tensor. This paper investigates how variations of the orientation are mapped to variation of the tensor, thereby giving an explicit equivariance relation. The results may be used in order to design tensor based algorithms for extraction of image features defined in terms of local variations of the orientation, \eg multi-dimensional curvature or circular symmetries. It is assumed that the variation of the local orientation can be described in terms of an orthogonal transformation group. Under this assumption a corresponding orthogonal transformation group, acting on the tensor, is constructed. Several correspondences between the two groups are demonstrated.

2345678 201 - 250 of 389
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