NDLI: Active skeleton for non-rigid object detection

Content Provider	IEEE Xplore Digital Library
Author	Xiang Bai Xinggang Wang Latecki, L.J. Wenyu Liu Zhuowen Tu
Copyright Year	2009
Description	Author affiliation: University of California, Los Angeles, USA (Zhuowen Tu) \|\| Huazhong Univ. of Sci.& Tech., China (Xiang Bai; Xinggang Wang; Wenyu Liu) \|\| Temple University, USA (Latecki, L.J.)
Abstract	We present a shape-based algorithm for detecting and recognizing non-rigid objects from natural images. The existing literature in this domain often cannot model the objects very well. In this paper, we use the skeleton (medial axis) information to capture the main structure of an object, which has the particular advantage in modeling articulation and non-rigid deformation. Given a set of training samples, a tree-union structure is learned on the extracted skeletons to model the variation in configuration. Each branch on the skeleton is associated with a few part-based templates, modeling the object boundary information. We then apply sum-and-max algorithm to perform rapid object detection by matching the skeleton-based active template to the edge map extracted from a test image. The algorithm reports the detection result by a composition of the local maximum responses. Compared with the alternatives on this topic, our algorithm requires less training samples. It is simple, yet efficient and effective. We show encouraging results on two widely used benchmark image sets: the Weizmann horse dataset [7] and the ETHZ dataset [16].
Starting Page	575
Ending Page	582
File Size	1083448
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459188
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Skeleton Object detection Layout Lighting Least squares methods Light sources Least squares approximation Automation Educational institutions Information science
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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