Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
A non-parametric filter for digital image restoration, using cluster analysis
Pattern Recognition Letters
Using resolution pyramids for watershed image segmentation
Image and Vision Computing
A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation
Computing in Science and Engineering
A Segmentation Method for Digital Images Based on Cluster Analysis
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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
Edge detection in contaminated images, using cluster analysis
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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In this paper we explore the use of the cluster analysis in segmentation problems, that is, identifying image points with an indication of the region or class they belong to. The proposed algorithm uses the well known agglomerative hierarchical cluster analysis algorithm in order to form clusters of pixels, but modified so as to cope with the high dimensionality of the problem. The results of different stages of the algorithm are saved, thus retaining a collection of segmented images ordered by degree of segmentation. This allows the user to view the whole collection and choose the one that suits him best for his particular application.