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| 2. |
- Andersson, Thord, et al.
(författare)
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A Fast Optimization Method for Level Set Segmentation
- 2009
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Ingår i: Image Analysis. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642022296 - 9783642022302 ; , s. 400-409
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Konferensbidrag (refereegranskat)abstract
- Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.
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| 3. |
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| 4. |
- Borga, Magnus, 1965-, et al.
(författare)
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A canonical correlation approach to exploratory data analysis in fMRI
- 2002
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A computationally efficient data-driven method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The proposed method is more robust and much more computationally efficient than independent component analysis, which previously has been applied in fMRI.
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| 5. |
- Borga, Magnus, et al.
(författare)
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An Adaptive Stereo Algorithm Based on Canonial Correlation Analysis
- 1998
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Ingår i: Proceedings of the Second IEEE International Conference on Intelligent Processing Systems. ; , s. 177-182
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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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| 8. |
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| 9. |
- 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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| 10. |
- Borga, Magnus, et al.
(författare)
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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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