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

Arif, Omar

Softcover

ISBN 10: 3843350388 ISBN 13: 9783843350389

Publisher: 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.

About this title

About the Author

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.

Edition details

• Publisher: LAP LAMBERT Academic Publishing
• Publication date: 2010
• Language: English
• ISBN 10: 3843350388
• ISBN 13: 9783843350389
• Binding: Paperback
• Number of pages: 116