Region-based fit of color homogeneity measures for fuzzy image segmentation
Fuzzy Sets and Systems
A customized Gabor filter for unsupervised color image segmentation
Image and Vision Computing
Robust Vessel Segmentation Based on Multi-resolution Fuzzy Clustering
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
A First Study on Clustering Collections of Workflow Graphs
Provenance and Annotation of Data and Processes
Color image segmentation using morphological clustering and fusion with automatic scale selection
Pattern Recognition Letters
Region-based Deformable Net for automatic color image segmentation
Image and Vision Computing
Fuzzy Aggregation with Artificial Color filters
Information Sciences: an International Journal
Unsupervised image segmentation using a hierarchical clustering selection process
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A novel fuzzy segmentation approach for brain MRI
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Localization scale selection for scale-space segmentation
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Color image segmentation using parallel OptiMUSIG activation function
Applied Soft Computing
Multiscale roughness measure for color image segmentation
Information Sciences: an International Journal
Fuzzy graph modeling for text segmentation from land map images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
An efficient color quantization based on generic roughness measure
Pattern Recognition
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A multiresolution color image segmentation approach is presented that incorporates the main principles of region-based segmentation and cluster-analysis approaches. The contribution of This work may be divided into two parts. In the first part, a multiscale dissimilarity measure is proposed that makes use of a feature transformation operation to measure the interregion relations with respect to their proximity to the main clusters of the image. As a part of this process, an original approach is also presented to generate a multiscale representation of the image information using nonparametric clustering. In the second part, a graph theoretic algorithm is proposed to synthesize regions and produce the final segmentation results. The latter algorithm emerged from a brief analysis of fuzzy similarity relations in the context of clustering algorithms. This analysis indicates that the segmentation methods in general may be formulated sufficiently and concisely by means of similarity relations theory. The proposed scheme produces satisfying results and its efficiency is indicated by comparing it with: 1) the single scale version of dissimilarity measure and 2) several earlier graph theoretic merging approaches proposed in the literature. Finally, the multiscale processing and region-synthesis properties validate our method for applications, such as object recognition, image retrieval, and emulation of human visual perception.