[PDF.31dd] Robust Target Localization and Segmentation: Application of Kernel-based statistical methods to computer vision

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

[PDF.ho78] Robust Target Localization and Segmentation: Application of Kernel-based statistical methods to computer vision

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 |  2010-09-12 | Original language:English | PDF # 1 |  8.66 x.27 x5.91l,.40 | File type: PDF | 116 pages

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||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 f

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 algor...

You easily download any file type for your device.Robust Target Localization and Segmentation: Application of Kernel-based statistical methods to computer vision   |  Omar Arif. Just read it with an open mind because none of us really know.

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