NDLI: Non-orthogonal binary subspace and its applications in computer vision

Content Provider	IEEE Xplore Digital Library
Author	Hai Tao Crabb, R. Feng Tang
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Eng., California Univ., Santa Cruz, CA, USA (Hai Tao; Crabb, R.; Feng Tang)
Abstract	This paper presents a novel approach that represents an image or a set of images using a non-orthogonal binary subspace (NBS) spanned by box-like base vectors. These base vectors possess the property that the inner product operation with them can be computed very efficiently. We investigate the optimized orthogonal matching pursuit method for finding the best NBS base vectors. It is demonstrated in this paper how the NBS based expansion can be applied to speed up several common computer vision algorithms, including normalized cross correlation (NCC), sum of squared difference (SSD) matching, appearance subspace projection and subspace-based object recognition. Promising experimental results on facial and natural images are demonstrated in this paper.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Pattern Anal. and Machine Intelligence
Starting Page	864
Ending Page	870
File Size	248733
Page Count	7
File Format	PDF
ISBN	076952334X
ISSN	15505499
DOI	10.1109/ICCV.2005.169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Application software Computer vision NIST Face detection Object recognition Image reconstruction Matching pursuit algorithms Pixel Principal component analysis Object detection
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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