Distance measures for signal processing and pattern recognition
Signal Processing
Three-dimensional image segmentation using a split, merge and group approach
Pattern Recognition Letters
Comparison of edge detectors: a methodology and initial study
Computer Vision and Image Understanding
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of empirical discrepancy evaluation measures
Pattern Recognition Letters
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating edge detection through boundary detection
EURASIP Journal on Applied Signal Processing
Journal of Mathematical Imaging and Vision
Objective comparison of contour detection in noisy images
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Quantitative error measures for edge detection
Pattern Recognition
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We present in this article a comparative study of well-known supervised evaluation criteria that enable the quantification of the quality of contour detection algorithms. The tested criteria are often used or combined in the literature to create new ones. Though these criteria are classical ones, none comparison has been made, on a large amount of data, to understand their relative behaviors. The objective of this article is to overcome this lack using large test databases both in a synthetic and a real context allowing a comparison in various situations and application fields and consequently to start a general comparison which could be extended by any person interested in this topic. After a review of the most common criteria used for the quantification of the quality of contour detection algorithms, their respective performances are presented using synthetic segmentation results in order to show their performance relevance face to undersegmentation, oversegmentation, or situations combining these two perturbations. These criteria are then tested on natural images in order to process the diversity of the possible encountered situations. The used databases and the following study can constitute the ground works for any researcher who wants to confront a new criterion face to well-known ones.