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Robust Target Localization and Segmentation: Application of Kernel-based statistical methods to computer vision - Couverture souple

Arif, Omar

 
9783843350389: Robust Target Localization and Segmentation: Application of Kernel-based statistical methods to computer vision
Couverture souple
ISBN 10 : 3843350388 ISBN 13 : 9783843350389
Editeur : LAP LAMBERT Academic Publishing, 2010

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

Omar Arif carries out research in the areas of computer vision, machine learning and image processing. He completed his BE in computer engineering from National University of Sciences and Tech, Pakistan and MS and PHD in electrical engineering from Georgia Tech, USA.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3843350388
ISBN 13 
9783843350389
Reliure
Broché
Nombre de pages
116
Coordonnées du fabricant
non disponible
Personne responsable
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9781243781048: Robust Target Localization and Segmentation Using Statistical Methods

Edition présentée

ISBN 10 :  1243781041 ISBN 13 :  9781243781048
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Couverture souple