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Author Konishi, Scott ♦ Yuille, A. L. ♦ Coughlan, James ♦ Zhu, Song Chun
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Fort Collins ♦ Likely Position ♦ Corresponding Filter ♦ Local Edge Cue ♦ Pre-segmented Image ♦ Quantative Measure ♦ Higher-level Model ♦ Different Edge Detector ♦ Edge Detection ♦ Multiple Scale ♦ Statistical Inference ♦ Colour Image ♦ Different Detector ♦ Multi-level Processing ♦ Statistical Effectiveness ♦ Fundamental Bound ♦ Relative Effectiveness ♦ Information Theoretic Evaluation ♦ Proceeding Computer Vision ♦ Different Edge Cue ♦ Pattern Recognition Cvpr ♦ Information Theoretic Measure
Description We treat the problem of edge detection as one of statistical inference. Local edge cues, implemented by filters, provide information about the likely positions of edges which can be used as input to higher-level models. Different edge cues can be evaluated by the statistical effectiveness of their corresponding filters evaluated on a dataset of 100 pre-segmented images. We use information theoretic measures to determine the effectiveness of a variety of different edge detectors working at multiple scales on black and white and colour images. Our results give quantative measures for the advantages of multi-level processing, for the use of chromaticity in addition to greyscale, and for the relative effectiveness of different detectors. Proceedings Computer Vision and Pattern Recognition CVPR’99. Fort Collins, Colorado. 1999.
Proc. IEEE Conf. Comput. Vision and Pattern Recognition
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1999-01-01