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Efficient facet edge detection and quantitative performance evaluation
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DOI：10.1016/S0031-3203(01)00035-8.png)
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摘要
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In this paper. we first introduce a recursive procedure for efficiently computing cubic facet parameters for edge detection. The procedure allows to compute facet parameters in a fixed number of operations independent of kernel size. We then introduce an image independent quantitative criterion for analytically evaluating different edge detectors (both gradient and zero-crossing based method,,) without the need of ground-truth information. Our criterion is based on our observation that all edge detectors make a decision of whether a pixel is an edgel or not based on the result of convolution of the image with a kernel. The variance of the convolution output therefore directly affects the performance of an edge detector. We propose to analytically compute the variance of the convolution output and use it as a measure to characterize the performance of four well-known edge detectors. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
edge detection
facet model
performance evaluation
feature extraction
low level image processing
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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QUANTITATIVE DESIGN AND EVALUATION OF ENHANCEMENT-THRESHOLDING EDGE DETECTORS.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
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