Robust Target Localization and Segmentation: Application of Kernel-based statistical methods to computer vision
Free Shipping Included! Robust Target Localization and Segmentation: Application of Kernel-based statistical methods to computer vision by Arif Omar at EMS Linux. Hurry! Limited time offer. Offer valid only while supplies last. 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
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.
|Manufacturer:||LAP LAMBERT Academic Publishing|
|Publisher:||LAP LAMBERT Academic Publishing|
|Studio:||LAP LAMBERT Academic Publishing|
|Item Weight:||0.4 pounds|
|Item Size:||0.27 x 8.66 x 8.66 inches|
|Package Weight:||0.49 pounds|
|Package Size:||5.91 x 0.27 x 0.27 inches|
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