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Title: Sparsity Analysis for Computer Vision Applications
Authors: CHENG BIN
Keywords: Sparsity Analysis, Computer Vision
Issue Date: 24-Jan-2013
Citation: CHENG BIN (2013-01-24). Sparsity Analysis for Computer Vision Applications. ScholarBank@NUS Repository.
Abstract: The research on sparse modeling has a long history. Recently research shows that sparse modeling appears to be biologically plausible as well as empirically effective in fields as diverse as computer vision, signal processing, natural language processing and machine learning. It has been proven to be an extremely powerful tool for acquiring, representing and compressing high-dimensional signals, and providing high performance for noise reduction, pattern classification, blind sourse separation and so on. In this dissertation, we study the sparse representations of high-dimensional signals for various learning and vision tasks, including graph learning, image segmentation and face recognition.
Appears in Collections:Ph.D Theses (Open)

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