Robust Target Localization and Segmentation
Language: English
Published by LAP LAMBERT Academic Publishing Sep 2010, 2010
- Softcover
- New
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
AbeBooks seller since January 11, 2012
Condition: New
£ 43.28
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Add to basketItem description from seller
This item is printed on demand - it takes 3-4 days longer - Neuware -This work aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used. The de-noising and clustering capabilities of the kernel PCA procedure lead to a robust algorithm. In the second method, a robust density comparison framework is developed that is applied to visual tracking, where an object is tracked by minimizing the distance between a model distribution and given candidate distributions. The superior performance of kernel-based algorithms comes at a price of increased storage and computational requirements. A novel method is developed that takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to reduce the computational and storage requirements for kernel-based methods. 116 pp. Englisch.…
Seller Inventory # 9783843350389
- Title
- Robust Target Localization and Segmentation
- Author
- Omar Arif
- Publisher
- LAP LAMBERT Academic Publishing Sep 2010
- Publication year
- 2010
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3843350388
- ISBN 13
- 9783843350389
- Item weight
- 191 grams
- Dimensions
- 220x150x7 mm
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
BuchWeltWeit Ludwig Meier e.K.
Bergisch Gladbach, Germany
AbeBooks seller since January 11, 2012
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