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

Arif, Omar

Brossura

ISBN 10: 3843350388 ISBN 13: 9783843350389

Casa editrice: LAP LAMBERT Academic Publishing, 2010

Riassunto

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.

Su questo libro

L'autore

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.

Dettagli dell'edizione

• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 3843350388
• ISBN 13: 9783843350389
• Rilegatura: Copertina flessibile
• Numero di pagine: 116
• Contatto del produttore: non disponibile
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