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Träfflista för sökning "WFRF:(Borga Magnus) srt2:(1995-1999)"

Sökning: WFRF:(Borga Magnus) > (1995-1999)

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1.
  • Borga, Magnus, et al. (författare)
  • A Unified Approach to PCA, PLS, MLR and CCA
  • 1997
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • This paper presents a novel algorithm for analysis of stochastic processes. The algorithm can be used to find the required solutions in the cases of principal component analysis (PCA), partial least squares (PLS), canonical correlation analysis (CCA) or multiple linear regression (MLR). The algorithm is iterative and sequential in its structure and uses on-line stochastic approximation to reach an equilibrium point. A quotient between two quadratic forms is used as an energy function and it is shown that the equilibrium points constitute solutions to the generalized eigenproblem.
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2.
  • Borga, Magnus, et al. (författare)
  • An Adaptive Stereo Algorithm Based on Canonial Correlation Analysis
  • 1998
  • Ingår i: Proceedings of the Second IEEE International Conference on Intelligent Processing Systems. ; , s. 177-182
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a novel algorithm that uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semitransparent images.
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4.
  • Borga, Magnus, et al. (författare)
  • Estimating Multiple Depths in Semi-transparent Stereo Images
  • 1999
  • Ingår i: Proceedings of the 11th Scandinavian Conference on Image Analysis.
  • Konferensbidrag (refereegranskat)abstract
    • A stereo algorithm that can estimate multiple depths in semi-transparent images is presented. The algorithm is based on a combination of phase analysis and canonical correlation analysis. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. For images with different but constant depths, a simple reconstruction procedure is suggested.
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6.
  • Borga, Magnus, 1965- (författare)
  • Learning Multidimensional Signal Processing
  • 1998
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • The subject of this dissertation is to show how learning can be used for multidimensional signal processing, in particular computer vision. Learning is a wide concept, but it can generally be defined as a system’s change of behaviour in order to improve its performance in some sense.Learning systems can be divided into three classes: supervised learning, reinforcement learning and unsupervised learning. Supervised learning requires a set of training data with correct answers and can be seen as a kind of function approximation. A reinforcement learning system does not require a set of answers. It learns by maximizing a scalar feedback signal indicating the system’s performance. Unsupervised learning can be seen as a way of finding a good representation of the input signals according to a given criterion.In learning and signal processing, the choice of signal representation is a central issue. For high-dimensional signals, dimensionality reduction is often necessary. It is then important not to discard useful information. For this reason, learning methods based on maximizing mutual information are particularly interesting.A properly chosen data representation allows local linear models to be used in learning systems. Such models have the advantage of having a small number of parameters and can for this reason be estimated by using relatively few samples. An interesting method that can be used to estimate local linear models is canonical correlation analysis (CCA). CCA is strongly related to mutual information. The relation between CCA and three other linear methods is discussed. These methods are principal component analysis (PCA), partial least squares (PLS) and multivariate linear regression (MLR). An iterative method for CCA, PCA, PLS and MLR, in particular low-rank versions of these methods, is presented.A novel method for learning filters for multidimensional signal processing using CCA is presented. By showing the system signals in pairs, the filters can be adapted to detect certain features and to be invariant to others. A new method for local orientation estimation has been developed using this principle. This method is significantly less sensitive to noise than previously used methods.Finally, a novel stereo algorithm is presented. This algorithm uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semi-transparent images.
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7.
  • Borga, Magnus (författare)
  • Reinforcement Learning Using Local Adaptive Models
  • 1995
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • In this thesis, the theory of reinforcement learning is described and its relation to learning in biological systems is discussed. Some basic issues in reinforcement learning, the credit assignment problem and perceptual aliasing, are considered. The methods of temporal difference are described. Three important design issues are discussed: information representation and system architecture, rules for improving the behaviour and rules for the reward mechanisms. The use of local adaptive models in reinforcement learning is suggested and exemplified by some experiments. This idea is behind all the work presented in this thesis. A method for learning to predict the reward called the prediction matrix memory is presented. This structure is similar to the correlation matrix memory but differs in that it is not only able to generate responses to given stimuli but also to predict the rewards in reinforcement learning. The prediction matrix memory uses the channel representation, which is also described. A dynamic binary tree structure that uses the prediction matrix memories as local adaptive models is presented. The theory of canonical correlation is described and its relation to the generalized eigenproblem is discussed. It is argued that the directions of canonical correlations can be used as linear models in the input and output spaces respectively in order to represent input and output signals that are maximally correlated. It is also argued that this is a better representation in a response generating system than, for example, principal component analysis since the energy of the signals has nothing to do with their importance for the response generation. An iterative method for finding the canonical correlations is presented. Finally, the possibility of using the canonical correlation for response generation in a reinforcement learning system is indicated.
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8.
  • Knutsson, Hans, et al. (författare)
  • Generalized Eigenproblem for Stochastic Process Covariances
  • 1996
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • 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.
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9.
  • Knutsson, Hans, et al. (författare)
  • Learning Canonical Correlations
  • 1995
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • 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.
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10.
  • Knutsson, Hans, 1950-, et al. (författare)
  • Learning Multidimensional Signal Processing
  • 1998
  • Ingår i: Proceedings of the 14th International Conference on Pattern Recognition, vol 2. - Linköping, Sweden : Linköping University, Department of Electrical Engineering. ; , s. 1416-1420
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • 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.
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  • Resultat 1-10 av 13

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