{"product_id":"robust-target-localization-and-segmentation-von-omar-arif","title":"Robust Target Localization and Segmentation","description":"\u003cp\u003eThis 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.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783843350389\"\u003e\u003ch3\u003eApplication of Kernel-based statistical methods to computer vision\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9783843350389","offer_id":39470033666141,"sku":"9783843350389","price":49.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/94b71345-58b1-4dc3-b090-b0b538022fe9.jpg?v=1756186915","url":"https:\/\/shop.autorenwelt.de\/products\/robust-target-localization-and-segmentation-von-omar-arif","provider":"Autorenwelt Shop","version":"1.0","type":"link"}