NDLI: Integrating Color and Gradient into Real-time Curve Tracking

Content Provider	IEEE Xplore Digital Library
Author	Huiqiong Chen Qigang Gao
Copyright Year	2008
Description	Author affiliation: Dalhousie Univ., Halifax, NS (Huiqiong Chen; Qigang Gao)
Abstract	Curve detection is one of the fundamental steps in computer vision applications. Conventional edge detectors provide only an output of edge pixels; curve matching is then needed to fit edge pixels into curves. Despite having achieved some success, it suffers constraints for applications that require real-time and robust image analysis, such as robot vision and video surveillance. Gao and Wang [11] developed a curve tracking algorithm for detecting perceptually sound curves by choosing initial edge pixel first and then do perceptual contour following based on gradient properties. It combines selective edge detection and curve extraction into one process for fast curve detection. In this paper, we extend this approach by integrating color and gradient properties for enhancing decision making when choosing the most relevant edge pixels for curve tracking. We provide experiments to demonstrate the performance improvement by comparing the new curve tracker with other edge detection techniques and the previous curve tracking algorithm.
Starting Page	294
Ending Page	300
File Size	877145
Page Count	7
File Format	PDF
ISBN	9780769531533
DOI	10.1109/CRV.2008.50
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Image edge detection color Robot vision systems Decision making curve tracking Application software Image color analysis real-time Detectors GET Robustness Curve fitting Pixel
Content Type	Text
Resource Type	Article

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