| 21. |
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| 22. |
- Borga, Magnus, et al.
(författare)
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Estimating Multiple Depths in Semi-transparent Stereo Images
- 1999
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Ingår i: Proceedings of the 11th Scandinavian Conference on Image Analysis.
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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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| 23. |
- Borga, Magnus, et al.
(författare)
-
Finding Efficient Nonlinear Visual Operators using Canonical Correlation Analysis
- 2000
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Ingår i: Proceedings of the SSAB Symposium on Image Analysis. - Linköping : Linköpings universitet. ; , s. 13-16
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 24. |
- Borga, Magnus, et al.
(författare)
-
FSED - feature selective edge detection
- 2000
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Ingår i: Proceedings of 15th International Conference on Pattern Recognition, Barcelona. - : IEEE. - 0769507506
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Konferensbidrag (refereegranskat)abstract
- We present a method that finds edges between certain image features, e.g. gray-levels, and disregards edges between other features. The method uses a channel representation of the features and performs normalized convolution using the channel values as certainties. This means that areas with certain features can be disregarded by the edge filter. The method provides an important tool for finding tissue specific edges in medical images, as demonstrated by an MR-image example.
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| 25. |
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| 26. |
- Borga, Magnus
(författare)
-
Hierarchical Reinforcement Learning
- 1993
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Ingår i: ICANN'93 eds S. Gielen and B. Kappen.
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Konferensbidrag (refereegranskat)abstract
- A hierarchical representation of the input-output transition function in a learning system is suggested. The choice of either representing the knowledge in a learning system as a discrete set of input-output pairs or as a continuous input-output transition function is discussed. The conclusion that both representations could be efficient, but at different levels of abstraction is made. The difference between strategies and actions is defined. An algorithm for using adaptive critic methods in a two-level reinforcement learning system is presented. Simulations of a one dimensional hierarchical reinforcement learning system is presented.
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| 27. |
- Borga, Magnus, et al.
(författare)
-
Improvement in Magnetic Resonance Imaging Relating to Correction of Chemical Shift Artifact and Intensity Inhomogeneity
- 2011
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Patent (populärvet., debatt m.m.)abstract
- Present invention discloses systems and methods for improvement of magnetic resonance images. Correction of a chemical shift artefact in an image acquired from a magnetic resonance imaging system is obtained by a system and a method involving iterative - compensation for the misregistration effect in an image domain. Correction of an intensity inhomogeneity in such images is obtained by a system and a method involving locating voxels corresponding to pure adipose tissue and estimating correction field from these points.
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| 28. |
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| 29. |
- Borga, Magnus, 1965-
(författare)
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Learning Multidimensional Signal Processing
- 1998
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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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| 30. |
- Borga, Magnus, 1965-
(författare)
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MRI adipose tissue and muscle composition analysis : a review of automation techniques
- 2018
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Ingår i: British Journal of Radiology. - London, United Kingdom : British Institute of Radiology. - 0007-1285 .- 1748-880X. ; 91:1089
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Forskningsöversikt (refereegranskat)abstract
- MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.
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