Color Segmentation Using LVQ Neural Networks

Intelligent Systems Reference Library - Engineering Applications of Soft Computing ◽

10.1007/978-3-319-57813-2_4 ◽

2017 ◽

pp. 59-75

Author(s):

Margarita-Arimatea Díaz-Cortés ◽

Erik Cuevas ◽

Raúl Rojas

Keyword(s):

Neural Networks ◽

Color Segmentation

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RoboCup Small-Size League: Using Neural Networks to Learn Color Segmentation during Visual Processing

2008 IEEE Latin American Robotic Symposium ◽

10.1109/lars.2008.29 ◽

2008 ◽

Cited By ~ 1

Author(s):

Ernesto Torres ◽

Alfredo Weitzenfeld

Keyword(s):

Neural Networks ◽

Visual Processing ◽

Color Segmentation

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LVQ Neural Networks in Color Segmentation

Soft Computing Methods for Practical Environment Solutions ◽

10.4018/978-1-61520-893-7.ch004 ◽

2010 ◽

pp. 45-63

Author(s):

Erik Cuevas ◽

Daniel Zaldivar ◽

Marco Perez-Cisneros ◽

Marco Block

Keyword(s):

Neural Networks ◽

Light Intensity ◽

Vector Quantization ◽

Learning Vector Quantization ◽

Color Images ◽

Color Segmentation ◽

Pixel Classification ◽

Input Space ◽

Segmentation Algorithms ◽

Image Pixels

Segmentation in color images is a complex and challenging task in particular to overcome changes in light intensity caused by noise and shadowing. Most of the segmentation algorithms do not tolerate variations in color hue corresponding to the same object. By means of the Learning Vector Quantization (LVQ) networks, neighboring neurons are able to learn how to recognize close sections of the input space. Neighboring neurons would thus correspond to color regions illuminated in different ways. This chapter presents an image segmentator approach based on LVQ networks which considers the segmentation process as a color-based pixel classification. The segmentator operates directly upon the image pixels using the classification properties of the LVQ networks. The algorithm is effectively applied to process sampled images showing its capacity to satisfactorily segment color despite remarkable illumination differences.

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