| 11. |
- Knutsson, Hans, et al.
(författare)
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Learning Visual Operators from Examples : A New Paradigm in Image Processing
- 1999
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Ingår i: Proceedings of the 10th International Conference on Image Analysis and Processing (ICIAP'99).
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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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| 12. |
- Landelius, Tomas, et al.
(författare)
-
On-Line Singular Value Decomposition of Stochastic Process Covariances
- 1995
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents novel algorithms for finding the singular value decomposition (SVD) of a general covariance matrix by stochastic approximation. General in the sense that also non-square, between sets, covariance matrices are dealt with. For one of the algorithms, convergence is shown using results from stochastic approximation theory. Proofs of this sort, establishing both the point of equilibrium and its domain of attraction, have been reported very rarely for stochastic, iterative feature extraction algorithms.
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| 13. |
- Landelius, Tomas, et al.
(författare)
-
Reinforcement Learning Trees
- 1996
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Two new reinforcement learning algorithms are presented. Both use a binary tree to store simple local models in the leaf nodes and coarser global models towards the root. It is demonstrated that a meaningful partitioning into local models can only be accomplished in a fused space consisting of both input and output. The first algorithm uses a batch like statistic procedure to estimate the reward functions in the fused space. The second one uses channel coding to represent the output- and input vectors allowing a simple iterative algorithm based on competing subsystems. The behaviors of both algorithms are illustrated in a preliminary experiment.
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