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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/18790
Title: Fast Head-and-shoulder Segmentation
Authors: Deng, Xiaowei
Advisor: Wu, Xiaolin
Department: Electrical and Computer Engineering
Keywords: head-and-shoulder;segmentation;learning-based;dynamic programming
Publication Date: 2016
Abstract: Many tasks of visual computing and communications such as object recognition, matting, compression, etc., need to extract and encode the outer boundary of the object in a digital image or video. In this thesis, we focus on a particular video segmentation task and propose an efficient method for head-and-shoulder of humans through video frames. The key innovations for our work are as follows: (1) a novel head descriptor in polar coordinate is proposed, which can characterize intrinsic head object well and make it easy for computer to process, classify and recognize. (2) a learning-based method is proposed to provide highly precise and robust head-and-shoulder segmentation results in applications where the head-and-shoulder object in the question is a known prior and the background is too complex. The efficacy of our method is demonstrated on a number of challenging experiments.
URI: http://hdl.handle.net/11375/18790
Appears in Collections:Open Access Dissertations and Theses

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