A dendritic lattice neural network for color image segmentation

Gonzalo Urcid; Luis David Lara-Rodríguez; Elizabeth López-Meléndez

doi:10.1117/12.2188795

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22 September 2015 A dendritic lattice neural network for color image segmentation

Gonzalo Urcid, Luis David Lara-Rodríguez, Elizabeth López-Meléndez

Proceedings Volume 9599, Applications of Digital Image Processing XXXVIII; 95992O (2015) https://doi.org/10.1117/12.2188795
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

A two-layer dendritic lattice neural network is proposed to segment color images in the Red-Green-Blue (RGB) color space. The two layer neural network is a fully interconnected feed forward net consisting of an input layer that receives color pixel values, an intermediate layer that computes pixel interdistances, and an output layer used to classify colors by hetero-association. The two-layer net is first initialized with a finite small subset of the colors present in the input image. These colors are obtained by means of an automatic clustering procedure such as k-means or fuzzy c-means. In the second stage, the color image is scanned on a pixel by pixel basis where each picture element is treated as a vector and feeded into the network. For illustration purposes we use public domain color images to show the performance of our proposed image segmentation technique.

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Gonzalo Urcid, Luis David Lara-Rodríguez, and Elizabeth López-Meléndez "A dendritic lattice neural network for color image segmentation", Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 95992O (22 September 2015); https://doi.org/10.1117/12.2188795

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