Comparison of Edge Detector Performance through Use in an Object Recognition Task

This paper presents an empirical evaluation methodology for edge detectors. Edge detector performance is measured using a particular edge-based object recognition algorithm as a “higher-level” task. A detector's performance is ranked according to the object recognition performance that it gener...

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Bibliographic Details
Published inComputer vision and image understanding Vol. 84; no. 1; pp. 160 - 178
Main Authors Shin, Min C, Goldgof, Dmitry B, Bowyer, Kevin W
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.10.2001
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Summary:This paper presents an empirical evaluation methodology for edge detectors. Edge detector performance is measured using a particular edge-based object recognition algorithm as a “higher-level” task. A detector's performance is ranked according to the object recognition performance that it generates. We have used a challenging train and test dataset containing 110 images of jeep-like images. Six edge detectors are compared and results suggest that (1) the SUSAN edge detector performs best and (2) the ranking of various edge detectors is different from that found in other evaluations.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1077-3142
1090-235X
DOI:10.1006/cviu.2001.0932