Segmentation of edges into lines and arcs
Image and Vision Computing
Distance measures for signal processing and pattern recognition
Signal Processing
Three-dimensional image segmentation using a split, merge and group approach
Pattern Recognition Letters
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Multimedia Tools and Applications
Shape Similarity Measures, Properties and Constructions
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Shape Matching: Similarity Measures and Algorithms
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
An empirical approach to grouping and segmentation
An empirical approach to grouping and segmentation
Binary-image comparison with local-dissimilarity quantification
Pattern Recognition
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Comparative study of contour detection evaluation criteria based on dissimilarity measures
Journal on Image and Video Processing - Regular
A multidimensional segmentation evaluation for medical image data
Computer Methods and Programs in Biomedicine
Multi-scale Analysis of Discrete Contours for Unsupervised Noise Detection
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Curvature estimation in noisy curves
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
General adaptive neighborhood image restoration, enhancement and segmentation
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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An image segmentation process often results in a special spatial set, called a mosaic, as the subdivision of a domain S within the n-dimensional Euclidean space. In this paper, S will be a compact domain and the study will be focused on finite Jordan mosaics, that is to say mosaics with a finite number of regions and where the boundary of each region is a Jordan hypersurface.The first part of this paper addresses the problem of comparing a Jordan mosaic to a given reference Jordan mosaic and introduces the 驴 dissimilarity criterion. The second part will show that the 驴 dissimilarity criterion can be used to perform the evaluation of image segmentation processes. It will be compared to classical criterions in regard to several geometric transformations. The pros and cons of these criterions are presented and discussed, showing that the 驴 dissimilarity criterion outperforms the other ones.