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Proceedings Paper

Unsupervised color image segmentation using a lattice algebra clustering technique
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Paper Abstract

In this paper we introduce a lattice algebra clustering technique for segmenting digital images in the Red-Green- Blue (RGB) color space. The proposed technique is a two step procedure. Given an input color image, the first step determines the finite set of its extreme pixel vectors within the color cube by means of the scaled min-W and max-M lattice auto-associative memory matrices, including the minimum and maximum vector bounds. In the second step, maximal rectangular boxes enclosing each extreme color pixel are found using the Chebychev distance between color pixels; afterwards, clustering is performed by assigning each image pixel to its corresponding maximal box. The two steps in our proposed method are completely unsupervised or autonomous. Illustrative examples are provided to demonstrate the color segmentation results including a brief numerical comparison with two other non-maximal variations of the same clustering technique.

Paper Details

Date Published: 2 November 2011
PDF: 10 pages
Proc. SPIE 8011, 22nd Congress of the International Commission for Optics: Light for the Development of the World, 80117D (2 November 2011); doi: 10.1117/12.902214
Show Author Affiliations
Gonzalo Urcid, Instituto Nacional de Astrofísica, Óptica y Electrónica (Mexico)
Gerhard X. Ritter, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 8011:
22nd Congress of the International Commission for Optics: Light for the Development of the World
Ramón Rodríguez-Vera; Ramón Rodríguez-Vera; Ramón Rodríguez-Vera; Rufino Díaz-Uribe; Rufino Díaz-Uribe; Rufino Díaz-Uribe, Editor(s)

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