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 |
Files in This Item:
File | Description | Size | Format | |
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thesis.pdf | Main article | 9.97 MB | Adobe PDF | View/Open |
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