Translator Disclaimer
22 September 2015 A dendritic lattice neural network for color image segmentation
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Color image segmentation algorithm based on neural networks
Proceedings of SPIE (October 11 2000)
Feature encoding for color image segmentation
Proceedings of SPIE (September 21 2001)
Color image segmentation: a review
Proceedings of SPIE (February 26 2010)

Back to Top