Please use this identifier to cite or link to this item:
Title: Video object segmentation with occlusion map
Authors: Xiong, H
Wang, Z
He, R
Feng, D
Keywords: Feature extraction
Image motion analysis
Image segmentation
Image sensors
Image sequences
Video signal processing
Issue Date: 2012
Source: Proceedings of the International Conference on Digital Image Computing Techniques and Applications (DICTA'2012), Fremantle, WA, Australia, 3-5 Dec. 2012, p. 1-7 How to cite?
Abstract: Extracting foreground objects from a video captured by a hand-held camera has been a new challenge in video segmentation, since most of the existing approaches generally work well provided that certain assumptions on the background scene or camera motion (e.g. still surveillance cameras) are imposed. While some approaches exploit several clues such as depth and motion to extract the foreground layer from handheld camera videos, we propose to leverage the advances in high quality interactive image segmentation. That is, we treat each video frame as an individual image and segment foreground objects with interactive image segmentation algorithms. In order to simulate user interactions, we derive reliable occlusion map for foreground objects and use the occlusion map as the "seeding" interactive input to an interactive image segmentation approach. In this paper, we employ an optical flow based occlusion detection approach for extracting the occlusion map and Geodesic star convexity based interactive image segmentation approach. In order to obtain accurate "seeding" user interactions, both forward and backward occlusion maps are computed and utilized. As a result, our approach is able to extract the whole objects having only partial movements, which overcomes the limitation of the stateof- the-art algorithm. Experimental results demonstrate both the effectiveness and efficiency of our proposed approach.
ISBN: 978-1-4673-2180-8
978-1-4673-2179-2 (E-ISBN)
DOI: 10.1109/DICTA.2012.6411684
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Mar 18, 2018

Page view(s)

Last Week
Last month
Citations as of Mar 12, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.