Paper
3 April 2008 Techniques for detection and classification of edges in color images
Author Affiliations +
Abstract
We introduce a novel method of splitting up color spaces into different components and then performing edge detection on individual color planes. The two general approaches taken for this are monochromatic and vector based. Also a new color space will be introduced in this paper, which is an improved version of the PCA algorithm. By analyzing the results of these algorithms we are able to determine which color space and edge detector is best suited for each algorithm. We test these methods using a number of well known edge detectors and color spaces. All the algorithms are tested on 17 different color images (12 natural, 5 synthetic). To analyze the results we use Pratt's Figure of Merit and Bovik's SSIM measures.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Karen Panetta, Sadaf Qazi, and Sos Agaian "Techniques for detection and classification of edges in color images", Proc. SPIE 6982, Mobile Multimedia/Image Processing, Security, and Applications 2008, 69820W (3 April 2008); https://doi.org/10.1117/12.777703
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CITATIONS
Cited by 9 scholarly publications.
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KEYWORDS
RGB color model

Edge detection

Principal component analysis

Sensors

Image quality

Detection and tracking algorithms

Image fusion

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