Vector quantization and signal compression
Vector quantization and signal compression
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Towards physics-based segmentation of photographic color images
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Color image segmentation based on edge-preservation smoothing and soft C-means clustering
Machine Graphics & Vision International Journal - Special issue on latest results in colour image processing and applications
ACODF: a novel data clustering approach for data mining in large databases
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
Image segmentation by unsupervised sparse clustering
Pattern Recognition Letters
Text segmentation in color images using tensor voting
Image and Vision Computing
High capacity data embedding using colour palette decomposition
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
IMPROVING GESTURE RECOGNITION IN THE ARABIC SIGN LANGUAGE USING TEXTURE ANALYSIS
Applied Artificial Intelligence
High capacity data embedding using Colour Palette Decomposition
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
A two-level strategy for segmenting center of interest from pictures
Expert Systems with Applications: An International Journal
A tensor voting for corrupted region inference and text image segmentation
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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In this work, we present an original technique for unsupervised segmentation of color images which is based on an extension, for an use in the u1v1 chromaticity diagram, of the well-known k -means algorithm, widely adopted in cluster analysis. We suggest exploiting the separability of color information which, represented in a suitable 3D space, may be "projected" onto a 2D chromatic subspace and onto a 1D luminance subspace. One can first compute the chromaticity coordinates ( u1v1) of colors and find representative clusters in such a 2D space, by using a 2D k means algorithm, and then associate these clusters with appropriate luminance values, by using a 1D k means algorithm, a simple dimensionally reduced version of the previous one. Experimental evidence of the effectiveness of our technique is reported.