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. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/18790 |
| Date | January 2016 |
| Creators | Deng, Xiaowei |
| Contributors | Wu, Xiaolin, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.002 seconds