NDLI: Model based segmentation and detection of affine transformed shapes in cluttered images

Content Provider	IEEE Xplore Digital Library
Author	Zhiqian Wang Ben-Arie, J.
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Illinois Univ., Chicago, IL, USA (Zhiqian Wang)
Abstract	This paper presents a novel approach for detection and segmentation of generic shapes in cluttered images. The underlying assumption is that man made objects frequently have surfaces which closely resemble standard model shapes such as rectangles, semicircles etc. Due to the transformation of optical imaging systems, a model shape can appear differently in the image with different orientations and aspect ratios. This set of possible appearances can be represented compactly by a few vectorial eigenbases that are derived from a small set of model shapes which are affine transformed in a wide range. The use of vectorial boundary information improves robustness to noise, background clutter and partial occlusion. The detection of generic shapes is realized by detecting local peaks of a similarity measure between the image edge map and an eigenspace combined set of the appearances. At each local maxima, a fast search approach based on a novel representation by angle space is employed to determine the best matching between models and the underlying subimage. Experiments are performed in various interfering distortions, and robust detection and recognition are achieved.
Starting Page	75
Ending Page	79
File Size	618460
Page Count	5
File Format	PDF
ISBN	0818688211
DOI	10.1109/ICIP.1998.998998
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-10-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image edge detection Object detection Shape measurement Optical distortion Noise robustness Background noise Noise shaping Layout Prototypes
Content Type	Text
Resource Type	Article

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