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  • 301.
    Johansson, Björn
    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.
    Goals and status within the IVSS project2006In: Seminar on "Cognitive vision in traffic analyses": Lund, Sweden, 2006Conference paper (Refereed)
  • 302.
    Johansson, Peter
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Plant Condition Measurement from Spectral Reflectance Data2010Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.

  • 303.
    Jonsson, Erik
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Channel-Coded Feature Maps for Computer Vision and Machine Learning2008Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis is about channel-coded feature maps applied in view-based object recognition, tracking, and machine learning. A channel-coded feature map is a soft histogram of joint spatial pixel positions and image feature values. Typical useful features include local orientation and color. Using these features, each channel measures the co-occurrence of a certain orientation and color at a certain position in an image or image patch. Channel-coded feature maps can be seen as a generalization of the SIFT descriptor with the options of including more features and replacing the linear interpolation between bins by a more general basis function.

    The general idea of channel coding originates from a model of how information might be represented in the human brain. For example, different neurons tend to be sensitive to different orientations of local structures in the visual input. The sensitivity profiles tend to be smooth such that one neuron is maximally activated by a certain orientation, with a gradually decaying activity as the input is rotated.

    This thesis extends previous work on using channel-coding ideas within computer vision and machine learning. By differentiating the channel-coded feature maps with respect to transformations of the underlying image, a method for image registration and tracking is constructed. By using piecewise polynomial basis functions, the channel coding can be computed more efficiently, and a general encoding method for N-dimensional feature spaces is presented.

    Furthermore, I argue for using channel-coded feature maps in view-based pose estimation, where a continuous pose parameter is estimated from a query image given a number of training views with known pose. The optimization of position, rotation and scale of the object in the image plane is then included in the optimization problem, leading to a simultaneous tracking and pose estimation algorithm. Apart from objects and poses, the thesis examines the use of channel coding in connection with Bayesian networks. The goal here is to avoid the hard discretizations usually required when Markov random fields are used on intrinsically continuous signals like depth for stereo vision or color values in image restoration.

    Channel coding has previously been used to design machine learning algorithms that are robust to outliers, ambiguities, and discontinuities in the training data. This is obtained by finding a linear mapping between channel-coded input and output values. This thesis extends this method with an incremental version and identifies and analyzes a key feature of the method -- that it is able to handle a learning situation where the correspondence structure between the input and output space is not completely known. In contrast to a traditional supervised learning setting, the training examples are groups of unordered input-output points, where the correspondence structure within each group is unknown. This behavior is studied theoretically and the effect of outliers and convergence properties are analyzed.

    All presented methods have been evaluated experimentally. The work has been conducted within the cognitive systems research project COSPAL funded by EC FP6, and much of the contents has been put to use in the final COSPAL demonstrator system.

  • 304.
    Jonsson, Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Object Recognition using Channel-Coded Feature Maps: C++ Implementation Documentation2008Report (Other academic)
    Abstract [en]

    This report gives an overview and motivates the design of a C++ framework for object recognition using channel-coded feature maps. The code was produced in connection to the work on my PhD thesis Channel-Coded Feature Maps for Object Recognition and Machine Learning. The package contains algorithms ranging from basic image processing routines to specific complex algorithms for creating channel-coded feature maps through piecewise polynomials. Much emphasis has been put in creating a flexible framework using virtual interfaces. This makes it easy e.g.~to switch between different image primitives detectors or learning methods in an object recognizer. Some common design choices include an image class with a convenient but fast pixel access, a configurable assert macro for error handling and a common base class for object ownership management. The main computer vision algorithms are channel-coded feature maps (CCFMs) including their derivatives, single-sided colored lines, object detection using an abstract hypothesize-verify framework and tracking and pose estimation using locally weighted regression and CCFMs. The code is considered as having alpha status at best. It is available under the GNU General Public License (GPL) and is mainly intended for future research on the subject.

  • 305.
    Jonsson, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Accurate Interpolation in Appearance-Based Pose Estimation2007In: Svenska Sällskapet för Automatiserad Bildanalys SSBA Symposium,2007, 2007, p. 13-16Conference paper (Other academic)
  • 306.
    Jonsson, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Accurate Interpolation in Appearance-Based Pose Estimation2007In: Image Analysis: 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007 / [ed] Bjarne Kjær Ersbøll and Kim Steenstrup Pedersen, Springer Berlin/Heidelberg, 2007, p. 1-10Conference paper (Refereed)
    Abstract [en]

    One problem in appearance-based pose estimation is the need for many training examples, i.e. images of the object in a large number of known poses. Some invariance can be obtained by considering translations, rotations and scale changes in the image plane, but the remaining degrees of freedom are often handled simply by sampling the pose space densely enough. This work presents a method for accurate interpolation between training views using local linear models. As a view representation local soft orientation histograms are used. The derivative of this representation with respect to the image plane transformations is computed, and a Gauss-Newton optimization is used to optimize all pose parameters simultaneously, resulting in an accurate estimate.

  • 307.
    Jonsson, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Correspondence-Free Associative Learning2006In: ICPR,2006, 2006Conference paper (Refereed)
  • 308.
    Jonsson, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Efficient computation of channel-coded feature maps through piecewise polynomials2009In: Image and Vision Computing, ISSN 0262-8856, Vol. 27, no 11, p. 1688-1694Article in journal (Refereed)
    Abstract [en]

    Channel-coded feature maps (CCFMs) represent arbitrary image features using multi-dimensional histograms with soft and overlapping bins. This representation can be seen as a generalization of the SIFT descriptor, where one advantage is that it is better suited for computing derivatives with respect to image transformations. Using these derivatives, a local optimization of image scale, rotation and position relative to a reference view can be computed. If piecewise polynomial bin functions are used, e.g. B-splines, these histograms can be computed by first encoding the data set into a histogram-like representation with non-overlapping multi-dimensional monomials as bin functions. This representation can then be processed using multi-dimensional convolutions to obtain the desired representation. This allows to reuse much of the computations for the derivatives. By comparing the complexity of this method to direct encoding, it is found that the piecewise method is preferable for large images and smaller patches with few channels, which makes it useful, e.g. in early steps of coarse-to-fine approaches.

  • 309.
    Jonsson, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Efficient Robust Mean Value Computation of 1D Features2005In: Efficient Robust Mean Value Computation of 1D Features,2005, 2005Conference paper (Refereed)
    Abstract [en]

     A robust mean value is often a good alternative to the standard mean value when dealing with data containing many outliers. An efficient method for samples of one-dimensional features and the truncated quadratic error norm is presented and compared to the method of channel averaging (soft histograms).

  • 310.
    Jonsson, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Reconstruction of probability density functions from channel representations2005In: Scandinavian Conference on Image Analysis, 2005, Vol. 3540, p. 491-500Conference paper (Refereed)
    Abstract [en]

    The channel representation allows the construction of soft histograms, where peaks can be detected with a much higher accuracy than in regular hard-binned histograms. This is critical in e.g. reducing the number of bins of generalized Hough transform methods. When applying the maximum entropy method to the channel representation, a minimum-information reconstruction of the underlying continuous probability distribution is obtained. The maximum entropy reconstruction is compared to simpler linear methods in some simulated situations. Experimental results show that mode estimation of the maximum entropy reconstruction outperforms the linear methods in terms of quantization error and discrimination threshold. Finding the maximum entropy reconstruction is however computationally more expensive.

  • 311.
    Jonsson, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Soft Histograms for Belief Propagation2006In: ECCV Workhop of the Representation and Use of Prior Knowledge in Vision,2006, 2006Conference paper (Refereed)
  • 312.
    Jonsson, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    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.
    Incremental Associative Learning2005Report (Other academic)
    Abstract [en]

      

  • 313.
    Järvinen, Arto
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Information representation in neural networks -- A survey1989Report (Other academic)
    Abstract [en]

    This report is a survey of information representations in both biological and artificial neural networks. The correct information representation is crucial for the dynamics and the adaptation algorithms of neural networks. A number of examples of existing information representations are given.

  • 314.
    Järvinen, Arto
    et al.
    n/a.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Study of information mapping in Kohonen--Networks1989Report (Other academic)
  • 315. Kalkan, Sinan
    et al.
    Calow, D.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Wörgötter, Florentin
    Lappe, M.
    Krüger, Norbert
    Optic Flow Statistics and Intrinsic Dimensionality2004In: BICS2004,2004, 2004Conference paper (Refereed)
  • 316.
    Karelid, Mikael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Image Enhancement over a Sequence of Images2008Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This Master Thesis has been conducted at the National Laboratory of Forensic Science (SKL) in Linköping. When images that are to be analyzed at SKL, presenting an interesting object, are of bad quality there may be a need to enhance them. If several images with the object are available, the total amount of information can be used in order to estimate one single enhanced image. A program to do this has been developed by studying methods for image registration and high resolution image estimation. Tests of important parts of the procedure have been conducted. The final results are satisfying and the key to a good high resolution image seems to be the precision of the image registration. Improvements of this part may lead to even better results. More suggestions for further improvementshave been proposed.

  • 317.
    Karlholm, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Associative Memories with Short--Range Higher Order Couplings1993In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 6, no 3, p. 409-421Article in journal (Refereed)
    Abstract [en]

    A study of recurrent associative memories with exclusively short-range connections is presented. To increase the capacity, higher order couplings are used. We study capacity and pattern completion ability of networks consisting of units with binary (±1) output. Results show that perfect learning of random patterns is difficult for very short coupling ranges, and that the average expected capacities (allowing small errors) in these cases are much smaller than the theoretical maximum, 2 bits per coupling. However, it is also shown that by choosing ranges longer than certain limit sizes, depending on network size and order, we can come close to the theoretical capacity limit. We indicate that these limit sizes increase very slowly with net size. Thus, couplings to at least 28 and 36 neighbors suffice for second order networks with 400 and 90,000 units, respectively. From simulations it is found that even networks with coupling ranges below the limit size are able to complete input patterns with more than 10% errors. Especially remarkable is the ability to correct inputs with large local errors (part of the pattern is masked). We present a local learning algorithm for heteroassociation in recurrent networks without hidden units. The algorithm is used in a multinet system to improve pattern completion ability on correlated patterns.

  • 318.
    Karlholm, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Efficient Spatiotemporal Filtering and Modelling1996Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The thesis describes novel methods for efficient spatiotemporal filtering and modeling. A multiresolution algorithm for energy-based estimation and representation of local spatiotemporal structure by second order symmetric tensors is presented. The problem of how to properly process estimates with varying degree of reliability is addressed. An efficient spatiotemporal implementation of a certainty-based signal modeling method called normalized convolution is described. As an application of the above results, a smooth pursuit motion tracking algorithm that uses observations of both target motion and position for camera head control and motion prediction is described. The target is detected using a novel motion field segmentation algorithm which assumes that the motion fields of the target and its immediate vicinity, at least occasionally, each can be modeled by a single parameterized motion model. A method to eliminate camera-induced background motion in the case of a pan/tilt rotating camera is suggested.

  • 319.
    Karlholm, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Local Signal Models for Image Sequence Analysis1998Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The thesis describes novel methods for image motion computation and template matching.

    A multiscale algorithm for energy-based estimation and representation of local spatiotemporal structure by second order symmetric tensors is presented. An efficient spatiotemporal implementation of a signalmodellingmethod called normalized convolution is described. This provides a means to handle signals with varying degree of reliability.

    As an application of the above results, a smooth pursuit motion tracking algorithm that uses observations of both targetmotion and position for camera head control and motion prediction is described. The target is detected using a novel motion field segmentation algorithm which assumes that the motion fields of the target and its immediate vicinity, at least occasionally, each can be modelled by a single parameterized motion model. A method to eliminate camera-induced background motion in the case of a pan/tilt rotating camera is suggested.

    In a second application, a high-precision image motion estimation algorithm performing clustering in motion parameter space is developed. The algorithm, which can handle multiple motions by simultaneous motion parameter estimation and image segmentation, iteratively maximizes the posterior probability of the motion parameter set given the observed local spatiotemporal structure tensor field. The probabilistic formulation provides a natural way to incorporate additional prior information about the segmentation of the scene into the objective function. A simple homotopy continuation method (embedding algorithm) is used to increase the likelihood of convergence to a nearoptimal solution.

    The final part of the thesis is concerned with tracking of (partially) occluded targets. An algorithm for target tracking in head-up display sequences is presented. The method generalizes cross-correlation coefficient matching by introducing a signal confidencebased distance metric. To handle target shape changes, a method for template mask shape-adaptation based on geometric transformation parameter optimisation is introduced. The presence of occluding objects makes local structure descriptors (e.g., the gradient) unreliable, which means that only pixelwise comparisons of target and template can be made, unless the local structure operators are modified to take into account the varying signal certainty. Normalized convolution provides the means for such a modification. This is demonstrated in a section on phase-based target tracking, which also contains a presentation of a generic method for tracking of occluded targets by combining normalized convolution with iterative reweighting.

  • 320.
    Karlholm, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Tracking of occluded targets in head-up display sequences1997Report (Other academic)
  • 321.
    Karlholm, Jörgen
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Westelius, Carl-Johan
    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.
    Object Tracking Based on the Orientation Tensor Concept1995In: SCIA9, Uppsala, 1995Conference paper (Other academic)
    Abstract [en]

    A scheme for performing generalized convolutions is presented. A flexibleconvolver, which runs on standard workstations, has been implemented. It isdesigned for maximum throughput and flexibility. The implementation incorporatesspatio-temporal convolutions with configurable vector combinations. Itcan handle general multi-linear operations, i.e. tensor operations on multidimensionaldata of any order. The input data and the kernel coefficients canbe of arbitrary vector length. The convolver is configurable for IIR filters inthe time dimension. Other features of the implemented convolver are scatteredkernel data, region of interest and subsampling. The implementation is doneas a C-library and a graphical user interface in AVS (Application VisualizationSystem).A scheme for performing generalized convolutions is presented. A flexible convolver, which runs on standard workstations, has been implemented. It is designed for maximum throughput and flexibility. The implementation incorporates spatio-temporal convolutions with configurable vector combinations. It can handle general multi-linear operations, i.e. tensor operations on multidimensional data of any order. The input data and the kernel coefficients can be of arbitrary vector length. The convolver is configurable for IIR filters in the time dimension. Other features of the implemented convolver are scattered kernel data, region of interest and subsampling. The implementation is done as a C-library and a graphical user interface in AVS (Application Visualization System).

  • 322.
    Karlholm, Jörgen
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Westelius, Carl-Johan
    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.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Object Tracking Based on the Orientation Tensor Concept1994Report (Other academic)
    Abstract [en]

    We apply the 3D-orientation tensor representation to construct an object tracking algorithm. 2D-line normal flow is estimated by computing the eigenvector associated with the largest eigenvalue of 3D (two spatial dimensions plus time) tensors with a planar structure. Object's true 2D velocity is computed by averaging tensors with consistent normal flows, generating a 3D line representation that corresponds to a 2D point in motion. Flow induced by camera rotation is compensated for by ignoring points with velocity consistent with the ego-rotation. A region-of-interest growing process based on motion consistency generates estimates of object size and position.

  • 323. Karlholm, Jörgen
    et al.
    Westelius, Carl-Johan
    n/a.
    Westin, Carl-Fredrik
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Object Tracking Based on the Orientation Tensor Concept1995In: Theory and Applications of Image Analysis II / [ed] Gunilla Borgefors, Singapore: World Scientific Publishing , 1995, p. 267-278Chapter in book (Other (popular science, discussion, etc.))
    Abstract [en]

    We apply the 3D-orientation tensor representation to construct an object tracking algorithm. 2D-line normal flow is estimated by computing the eigenvector associated with the largest eigenvalue of 3D (two spatial dimensions plus time) tensors with a planar structure. Object's true 2D velocity is computed by averaging tensors with consistent normal flows, generating a 3D line represention that corresponds to a 2D point in motion. Flow induced by camera rotation is compensated for by ignoring points with velocity consistent with the ego-rotation. A region-of-interest growing process based on motion consistency generates estimates of object size and position.  Introduction The literature on optical flow estimation is wast. Descriptions and performance studies of a number of different techniques are given in  and the monographs by Fleet  and Jahne. We will only briefly describe the particular methods used in the present study. Details on the tensor field represention a...

  • 324.
    Klomark, Marcus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Occupant Detection using Computer Vision2000Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of this master’s thesis was to study the possibility to use computer vision methods to detect and classify objects in the front passenger seat in a car. This work presents different approaches to solve this problem and evaluates the usefulness of each technique. The classification information should later be used to modulate the speed and the force of the airbag, to be able to provide each occupant with optimal protection and safety.

    This work shows that computer vision has a great potential in order to provide data, which may be used to perform reliable occupant classification. Future choice of method to use depends on many factors, for example costs and requirements on the system from laws and car manufacturers. Further, evaluation and tests of the methods in this thesis, other methods, the ABE approach and post-processing of the results should also be made before a reliable classification algorithm may be written.

  • 325.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    3-D Reconstruction by Fourier Techniques with Error Estimates1978Report (Other academic)
  • 326.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Tensor Representation of 3-D Structures1987In: 5th IEEE-ASSP and EURASIP Workshop on Multidimensional Signal Processing: Noordwijkerhout, The Netherlands, 1987Conference paper (Refereed)
  • 327.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Design of Convolution Kernels1982Report (Other academic)
    Abstract [en]

    Operators for extraction of local information are essential components in an image processing system. This paper concentrates on the design and evaluation of convolution kernel sets enabling easy estimation of local orientation and frequency.

    Consideration of interpolation properties and the limiting effects of the uncertainty principle leads to the definition of an "i deal" quadrature filter function. An optimization procedure is utilized to produce pairs of convolution kernels which implement an approximation of the desired function. A number of optimization results are presented.

    To evaluate the performance of the optimized kernels in an image processing task, a series of experiments have been carried out. Examples are given of local orientation and frequency estimates for images with different signal to noise ratios. An angle deviation measure is defined and avector averaging scheme is introduced to increase angle estimation accuracy. Using a OdB SNR testimage, orientation estimates are produced having an expected deviation of less than 7 degrees.

  • 328.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Filtering and reconstruction in image processing1982Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Image processing is a broad field posing a wide range of problems. The Work presented in this dissertation is mainly concerned with filter design subjectto different criteria and constraints.

    The first part describes the development of a new radiographic reconstruction method designated Ectomography. The method is novel in that it allows reconstruction of an arbitrarily thick layer of an object using limited viewing angle.

    The subject of the second partis estimation and filtering of local image information. Quadrature filters are designed enabling accurate orientation and frequency estimates. The extracted information is shown to provide a good basis fo r efficient image enhancement and coding procedures.

    List of papers
    1. Ectomography: A New Radiographic Method for Reproducing a Selected Slice of Varying Thickness
    Open this publication in new window or tab >>Ectomography: A New Radiographic Method for Reproducing a Selected Slice of Varying Thickness
    1980 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 21, no 4, p. 433-442Article in journal (Refereed) Published
    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.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-21580 (URN)7457172 (PubMedID)
    Available from: 2009-10-04 Created: 2009-10-04 Last updated: 2017-12-13Bibliographically approved
    2. Ectomography. A New Radiographic Reconstruction Method: I. Theory and Error Estimates
    Open this publication in new window or tab >>Ectomography. A New Radiographic Reconstruction Method: I. Theory and Error Estimates
    1980 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. BME--27, no 11, p. 640-645Article in journal (Refereed) Published
    Abstract [en]

    Radiographic technology has advanced considerably during the last years with the advent of reconstruction techniques allowing visualization of slices through the body. In spite of the advantage of computed tomography compared to conventional radiographic methods, there are still some shortcomings with the method If a different section of the body is desired, another recording has to be made, the width of the dice reconstructed is fixed, and a full 1800 view angle is required.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-21571 (URN)10.1109/TBME.1980.326704 (DOI)
    Available from: 2009-10-04 Created: 2009-10-04 Last updated: 2017-12-13
    3. Ectomography. A New Radiographic Reconstruction Method: II. Computer Simulated Experiments
    Open this publication in new window or tab >>Ectomography. A New Radiographic Reconstruction Method: II. Computer Simulated Experiments
    1980 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. BME--27, no 11, p. 649-655Article in journal (Refereed) Published
    Abstract [en]

    In a special radiographic process, ectomography, an image of a slice is produced by simple summation of a set of specially filtered component images, of which each represents one of at least 60 different projections of the object. After being digitized, they are stored, filtered, and summed in a computer. Images representing any slice of any thickness in the object may be produced from the same set of component images. All details within the slice are pictured correctly while details outside are almost completely eliminated.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-21581 (URN)10.1109/TBME.1980.326705 (DOI)
    Available from: 2009-10-04 Created: 2009-10-04 Last updated: 2017-12-13
    4. Design of Convolution Kernels
    Open this publication in new window or tab >>Design of Convolution Kernels
    1982 (English)Report (Other academic)
    Abstract [en]

    Operators for extraction of local information are essential components in an image processing system. This paper concentrates on the design and evaluation of convolution kernel sets enabling easy estimation of local orientation and frequency.

    Consideration of interpolation properties and the limiting effects of the uncertainty principle leads to the definition of an "i deal" quadrature filter function. An optimization procedure is utilized to produce pairs of convolution kernels which implement an approximation of the desired function. A number of optimization results are presented.

    To evaluate the performance of the optimized kernels in an image processing task, a series of experiments have been carried out. Examples are given of local orientation and frequency estimates for images with different signal to noise ratios. An angle deviation measure is defined and avector averaging scheme is introduced to increase angle estimation accuracy. Using a OdB SNR testimage, orientation estimates are produced having an expected deviation of less than 7 degrees.

    Publisher
    p. 61
    Series
    LiTH-ISY-I, ISSN 8765-4321 ; 0557
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-56437 (URN)LiTH-ISY-I-0557 (ISRN)
    Available from: 2010-05-12 Created: 2010-05-12 Last updated: 2010-05-12Bibliographically approved
    5. Anisotropic Non-Stationary Image Estimation and its Applications: Part I. Restoration of Noisy Images
    Open this publication in new window or tab >>Anisotropic Non-Stationary Image Estimation and its Applications: Part I. Restoration of Noisy Images
    1983 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. COM--31, no 3, p. 388-397Article in journal (Refereed) Published
    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.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-21582 (URN)10.1109/TCOM.1983.1095832 (DOI)
    Note
    ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Hans Knutsson, Roland Wilson and Gösta H. Granlund, Anisotropic Non-Stationary Image Estimation and its Applications: Part I. Restoration of Noisy Images, 1983, IEEE Transactions on Communications, (COM--31), 3, 388-397. http://dx.doi.org/10.1109/TCOM.1983.1095832 Available from: 2009-10-04 Created: 2009-10-04 Last updated: 2017-12-13Bibliographically approved
    6. Anisotropic Non-Stationary Image Estimation and its Applications: Part II. Predictive Image Coding
    Open this publication in new window or tab >>Anisotropic Non-Stationary Image Estimation and its Applications: Part II. Predictive Image Coding
    1983 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 31, no 3, p. 398-406Article in journal (Refereed) Published
    Abstract [en]

    A new predictive coder, based on an estimation method which adapts to line and edge features in images, is described. Quantization of the prediction error is performed by a two-level adaptive scheme: an adaptive transform coder, and a threshold coding in both transform and spatial domains. Control information, which determines the behavior of the predictor, is quantized using a simple variable rate technique. The results are improved by pre- and post-filtering using a related noncausal form of the estimator. Acceptable images have been produced in this way at bit rates of less than 0.5 bit/pixel.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-21587 (URN)
    Available from: 2009-10-04 Created: 2009-10-04 Last updated: 2017-12-13Bibliographically approved
  • 329.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Kernel Optimizatioin1995In: Signal Processing for Computer Vision / [ed] Gösta H. Granlund and Hans Knutsson, Dordrecht: Kluwer , 1995, p. 199-218Chapter in book (Refereed)
    Abstract [en]

    This chapter presents a method for obtaining an optimal n-dimensional set of filter coefficients for any given frequency response. An optimality criterion is defined that enables different frequencies to be given individual weights. Appropriate forms of frequency weight functions are discussed and a number of optimization examples are given.

  • 330.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Producing a Continuous and Distance Preserving 5-D Vector Representation of 3-D Orientation1985In: IEEE Computer Society Workshop on Computer Architecture for Pattern Analysis and Image Database Management - CAPAIDM: Miami Beach, Florida, 1985, p. 175-182Conference paper (Refereed)
  • 331.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Representing and Estimating 3-D Orientation Using Quadrature Filters1986In: Conference Publication No. 265, Second Int. Conf. on Image Processing and Its Applications: London, 1986, p. 87-91Conference paper (Refereed)
  • 332.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Representing Local Structure Using Tensors1989In: Proceedings of the 6th Scandinavian Conference on Image Analysis, Linköping: Linköping University Electronic Press , 1989, p. 244-251Conference paper (Other academic)
    Abstract [en]

    The fundamental problem of finding a suitable representation of the orientation of 3D surfaces is considered. A representation is regarded suitable if it meets three basic requirements: Uniqueness, Uniformity and Polar separability. A suitable tensor representation is given.

    At the heart of the problem lies the fact that orientation can only be defined mod 180± , i.e the fact that a 180± rotation of a line or a plane amounts to no change at all. For this reason representing a plane using its normal vector leads to ambiguity and such a representation is consequently not suitable. The ambiguity can be eliminated by establishing a mapping between R3 and a higherdimensional tensor space.

    The uniqueness requirement implies a mapping that map all pairs of 3D vectors x and -x onto the same tensor T. Uniformity implies that the  mapping implicitly carries a definition of distance between 3D planes (and lines) that is rotation invariant and monotone with the angle between the planes. Polar separability means that the norm of the representing tensor T is rotation invariant. One way to describe the mapping is that it maps a 3D sphere into 6D in such a way that the surface is uniformly uniformly stretched and all pairs of antipodal points maps onto the same tensor.

    It is demonstrated that the above mapping can be realized by sampling the 3D space using a specified class of symmetrically distributed quadrature filters. It is shown that 6 quadrature filters are necessary to realize the desired mapping, the orientations of the filters given by lines trough the vertices of an icosahedron. The desired tensor representation can be obtained by simply performing a weighted summation of the quadrature filter outputs. This situation is indeed satisfying as it implies a simple implementation of the theory and that requirements on computational capacity can be kept within reasonable limits.

    Noisy neigborhoods and/or linear combinations of tensors produced by the mapping will in general result in a tensor that has no direct counterpart in R3. In an adaptive hierarchical signal processing system, where information is flowing both up (increasing the level of abstraction) and down (for adaptivity and guidance), it is necessary that a meaningful inverse exists for each levelaltering operation. It is shown that the point in R3 that corresponds to the best approximation of a given tensor is  given by the largest eigenvalue times the corresponding eigenvector of the tensor.

  • 333.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    The meaninglessness of `Sit-and-stare' -- How Vision-Action-Understanding is inseparable1992In: Workshop on Vision: Ruzenagaard, Själlands Udde, 1992, p. 9-20Conference paper (Refereed)
  • 334.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Andersson, Magnus
    n/a.
    Robust N-Dimensional Orientation Estimation using Quadrature Filters and Tensor Whitening1994In: ICASSP: Adelaide, Australia, 1994Conference paper (Refereed)
    Abstract [en]

    In this paper it is shown how estimates of local structure and orientation can be obtained using a set of spherically separable quadrature filters. The method is applicable to signals of any dimensionality the only requirement being that the filter set spans the corresponding orientation space. The estimates produced are 2:nd order tensors, the size of the tensors corresponding to the dimensionality of the input signal. A central part of the algorithm is an operation termed Tensor Whitening reminiscent of classical whitening procedures. This operation compensates exactly for any biases introduced by non-uniform filter orientation distributions and/or non-uniform filter output certainties. Examples of processing of 2D-images, 3D-volumes and 2D-image sequences are given. Sensitivity to noise and missing filter outputs are analyzed in different situations. Estimation accuracy as a function of filter orientation distributions are studied. The studies provide evidence that the algorithm is robust and preferable to other algorithms in a wide range of situations.

  • 335.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Optimization of Sequential Filters1995In: Proceedings of the SSAB Symposium on Image Analysis: Linköping, Sweden, 1995, p. 87-90Conference paper (Refereed)
    Abstract [en]

    A recursive method for separation of spherically separable quadrature filters into simple kernels with mainly one dimensional extent has been worked out. The resulting filter responses are mapped to a non biased tensor representation where the local tensor constitutes a robust estimate of both the shape and the orientation (velocity) of the neighbourhood. The performance of this General Sequential Filter concept has exceeded the authors most optimistic expectations. A qualitative evaluation results in no detectable loss in accuracy when compared to conventional FIR (Finite Impulse Response) filters but the computation is performed 20-30 times faster. The magnitude of the attained speed-up implies that complex spatio-temporal analysis can be performed using standard hardware, such as a powerful workstation, in close to real time. Due to the soft implementation of the convolver and the tree structure of the sequential filtering approach the processing is simple to optimize for most standard hardware. The method used in the examples was implemented in AVS (Application Visualization System) using modules written in C.

  • 336.
    Knutsson, Hans
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wiklund, Johan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Automated generation of representations in vision2000In: International Conference on Pattern Recognition ICPR,2000, Barcelona, Spain: IEEE , 2000, p. 59-66 vol.3Conference paper (Refereed)
    Abstract [en]

    This paper presents a general strategy for automated generation of efficient representations in vision. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of situations that are dependent through the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. How visual operators and representations can be generated from examples are presented for a number of features, e.g. local orientation, disparity and motion. Interesting similarities to biological vision functions are observed. The results clearly demonstrates the potential of combining advanced filtering techniques and learning strategies based on canonical correlation analysis (CCA).

  • 337.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, The Institute of Technology.
    Haglund, Leif
    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.
    Orientation and Velocity1995In: Signal Processing for Computer Vision / [ed] Gösta H. Granlund and Hans Knutsson, Dordrecht: Kluwer , 1995, p. 219-258Chapter in book (Refereed)
    Abstract [en]

    This chapter introduces the use of tensors in estimation of local structure and orientation. The tensor representation is shown to be crucial to unambiguous and continuous representation of local orientation in multiple dimensions. In addition to orientation the tensor representation also conveys the degree and type of local anisotropy. The orientation estimation approach is developed in detail for two, three and four dimensions and is shown to be extendable to higher dimensions. Examples and performance measures are given for processing of images, volumes and time sequences.

  • 338.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Andersson, Mats T.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Kronander, Torbjörn
    SECTRA AB, Linköping, Sweden.
    Hemmendorff, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Spatio-temporal filtering of digital angiography image data1998In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 57, no 1-2, p. 115-123Article in journal (Refereed)
    Abstract [en]

    As welfare diseases become more common all over the world the demand for angiography examinations is increasing rapidly. The development of advanced medical signal processing methods has with few exceptions been concentrated towards CT and MR while traditional contrast based radiology depend on methods developed for ancient photography techniques despite the fact that angiography sequences are generally recorded in digital form. This article presents a new approach for processing of angiography sequences based on advanced image processing methods. The developed algorithm automatically processes angiography sequences containing motion artifacts that cannot be processed by conventional methods like digital subtraction angiography (DSA) and pixel shift due to non uniform motions. The algorithm can in simple terms be described as an ideal pixelshift filter carrying out shifts of different directions and magnitude according to the local motions in the image. In difference to conventional methods it is fully automatic, no mask image needs to be defined and the manual pixelshift operations, which are extremely time consuming, are eliminated. The algorithm is efficient and robust and is designed to run on standard hardware of a powerful workstation which excludes the need for expensive dedicated angiography platforms. Since there is no need to make additional recordings if the patient moves, the patient is exposed to less amount of radiation and contrast fluid. The most exciting benefits by this method are, however, that it opens up new areas for contrast based angiography that are not possible to process with conventional methods e.g. nonuniform motions and multiple layers of moving tissue. Advanced image processing methods provide significantly better image quality and noise suppression but do also provide the means to compute flow velocity and visualize the flow dynamics in the arterial trees by e.g. using color. Initial tests have proven that it is possible to discriminate capillary blood flow from angiography data which opens up interesting possibilities for estimating the blood flow in the heart muscle without use of nuclear methods.

  • 339.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Advanced Filter Design1999In: Proceedings of the 11th Scandinavian Conference on Image Analysis: Greenland, SCIA , 1999, p. 185-193Conference paper (Refereed)
    Abstract [en]

    This paper presents a general approach for obtaining optimal filters as well as filter sequences. A filter is termed optimal when it minimizes a chosen distance measure with respect to an ideal filter. The method allows specification of the metric via simultaneous weighting functions in multiple domains, e.g. the spatio-temporal space and the Fourier space. Metric classes suitable for optimization of localized filters for multidimensional signal processing are suggested and discussed.

    It is shown how convolution kernels for efficient spatio-temporal filtering can be implemented in practical situations. The method is based on applying a set of jointly optimized filter kernels in sequence. The optimization of sequential filters is performed using a novel recursive optimization technique. A number of optimization examples are given that demonstrate the role of key parameters such as: number of kernel coefficients, number of filters in sequence, spatio-temporal and Fourier space metrics.

    The sequential filtering method enables filtering using only a small fraction of the number of filter coefficients required using conventional filtering. In multidimensional filtering applications the method potentially outperforms both standard convolution and FFT based approaches by two-digit numbers.

  • 340.
    Knutsson, Hans
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    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.
    Multiple Space Filter Design1999In: Proceedings of the SSAB symposium on image analysis: Gothenburg, 1999Conference paper (Refereed)
    Abstract [en]

    This paper presents a general approach for obtaining optimal filters as well as filter sequences. A filter is termed optimal when it minimizes a chosen distance measure with respect to an ideal filter. The method allows specification of the metric via simultaneous weighting functions in multiple domains, e.g. the spatio-temporal space and the Fourier space. It is shown how convolution kernels for efficient spatio-temporal filtering can be implemented in practical situations. The method is based on applying a set of jointly optimized filter kernels in sequence. The optimization of sequential filters is performed using a novel recursive optimization technique. A number of optimization examples are given that demonstrate the role of key parameters such as: number of kernel coefficients, number of filters in sequence, spatio-temporal and Fourier space metrics. In multidimensional filtering applications the method potentially outperforms both standard convolution and FFT based approaches by two-digit numbers.

  • 341.
    Knutsson, Hans
    et al.
    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.
    Learning Visual Operators from Examples: A New Paradigm in Image Processing1999In: Proceedings of the 10th International Conference on Image Analysis and Processing (ICIAP'99): Venice, Italy, 1999Conference paper (Refereed)
    Abstract [en]

    This paper presents a general strategy for designing efficient visual operators. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of images displaying a strong dependence in the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. Visual operators learned from examples are presented, e.g. local shift invariant orientation operators and image content invariant disparity operators. Interesting similarities to biological vision functions are observed.

  • 342.
    Knutsson, Hans
    et al.
    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.
    Landelius, Tomas
    n/a.
    Generalized Eigenproblem for Stochastic Process Covariances1996Report (Other academic)
    Abstract [en]

    This paper presents a novel algorithm for finding the solution of the generalized eigenproblem where the matrices involved contain expectation values from stochastic processes. The algorithm is iterative and sequential to its structure and uses on-line stochastic approximation to reach an equilibrium point. A quotient between two quadratic forms is suggested as an energy function for this problem and is shown to have zero gradient only at the points solving the eigenproblem. Furthermore it is shown that the algorithm for the generalized eigenproblem can be used to solve three important problems as special cases. For a stochastic process the algorithm can be used to find the directions for maximal variance, covariance, and canonical correlation as well as their magnitudes.

  • 343.
    Knutsson, Hans
    et al.
    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.
    Landelius, Tomas
    n/a.
    Learning Canonical Correlations1995Report (Other academic)
    Abstract [en]

    This paper presents a novel learning algorithm that finds the linear combination of one set of multi-dimensional variates that is the best predictor, and at the same time finds the linear combination of another set which is the most predictable. This relation is known as the canonical correlation and has the property of being invariant with respect to affine transformations of the two sets of variates. The algorithm successively finds all the canonical correlations beginning with the largest one. It is shown that canonical correlations can be used in computer vision to find feature detectors by giving examples of the desired features. When used on the pixel level, the method finds quadrature filters and when used on a higher level, the method finds combinations of filter output that are less sensitive to noise compared to vector averaging.

  • 344.
    Knutsson, Hans
    et al.
    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.
    Landelius, Tomas
    n/a.
    Learning Multidimensional Signal Processing1998Report (Other academic)
    Abstract [en]

    This paper presents our general strategy for designing learning machines as well as a number of particular designs. The search for methods allowing a sufficient level of adaptivity are based on two main principles: 1. Simple adaptive local models and 2. Adaptive model distribution. Particularly important concepts in our work is mutual information and canonical correlation. Examples are given on learning feature descriptors, modeling disparity, synthesis of a global 3-mode model and a setup for reinforcement learning of online video coder parameter control.

  • 345.
    Knutsson, Hans
    et al.
    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.
    Landelius, Tomas
    n/a.
    Learning Multidimensional Signal Processing1998In: Proceedings of the 14th International Conference on Pattern Recognition, vol 2: Brisbane, Australia, 1998, Vol. II, p. 1416-1420Conference paper (Refereed)
    Abstract [en]

    This paper presents our general strategy for designing learning machines as well as a number of particular designs. The search for methods allowing a sufficient level of adaptivity are based on two main principles: 1. Simple adaptive local models and 2. Adaptive model distribution. Particularly important concepts in our work is mutual information and canonical correlation. Examples are given on learning feature descriptors, modeling disparity, synthesis of a global 3-mode model and a setup for reinforcement learning of online video coder parameter control.

  • 346.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Bårman, Håkan
    n/a.
    Haglund, Leif
    n/a.
    Robust Orientation Estimation in 2D, 3D and 4D Using Tensors1992In: Proceedings of Second International Conference on Automation, Robotics and Computer Vision, ICARCV'92: Singapore, 1992Conference paper (Refereed)
  • 347.
    Knutsson, Hans E.
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Edholm, Paul
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Petersson, Christer U.
    n/a.
    Ectomography. A New Radiographic Reconstruction Method: I. Theory and Error Estimates1980In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. BME--27, no 11, p. 640-645Article in journal (Refereed)
    Abstract [en]

    Radiographic technology has advanced considerably during the last years with the advent of reconstruction techniques allowing visualization of slices through the body. In spite of the advantage of computed tomography compared to conventional radiographic methods, there are still some shortcomings with the method If a different section of the body is desired, another recording has to be made, the width of the dice reconstructed is fixed, and a full 1800 view angle is required.

  • 348.
    Knutsson, Hans
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Edholm, Paul
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Aspects of 3-D Reconstruction by Fourier Techniques1980In: Digital Signal Processing: eds T. G. Constantinides and V. Cappellini / [ed] V. Cappellini and A.G. Constantinides, London: Academic Press , 1980Chapter in book (Refereed)
  • 349.
    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.
    Fourier Domain Design of Line and Edge Detectors1980In: Proceedings of the 5th International Conference on Pattern Recognition: Miami, Florida, 1980Conference paper (Refereed)
  • 350.
    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.
    Spatio-Temporal Analysis Using Tensors1989In: Sixth Multidimensional Signal Processing Workshop: Pacific Grove, California, 1989Conference paper (Refereed)
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