Authors:
Francisco Javier Díaz-Pernas
;
Míriam Antón-Rodríguez
;
Víctor Iván Serna-González
;
José Fernando Díez-Higuera
and
Mario Martínez-Zarzuela
Affiliation:
Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering School, University of Valladolid, Spain
Keyword(s):
Computer Vision, neural classifier, texture recognition, colour image segmentation, Boundary Contour System, Feature Contour System, ART, colour-opponent processes.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
A dynamic multiple scale neural model for recognising colour images of textured scenes is proposed. This model combines colour and textural information to recognise coloured textures through the operation of two main components: segmentation component formed by the Colour Opponent System (COS) and the Chromatic Segmentation System (CSS); and recognition component formed by pattern generation stages and Fuzzy ARTMAP neural network. Firstly, the COS module transforms the RGB chromatic input signals into a bio-inspired codification system (L, M, S and luminance signals), and then it generates the opponent channels (black-white, L-M and
S-(L+M)). The CSS module incorporates contour extraction, double opponency mechanisms and diffusion processes in order to generate coherent enhancing regions in colour image segmentation. These colour region enhancements along with the local textural features of the scene constitute the recognition pattern to be sent into the Fuzzy ARTMAP network. The s
tructure of the CSS architecture is based on BCS/FCS systems, thus, maintaining their essential qualities such as illusory contours extraction, perceptual grouping and discounting the illuminant. But base models have been extended to allow colour stimuli processing in order to obtain general purpose architecture for image segmentation with later applications on computer vision and object recognition. Some comparative testing with other models is included here in order to prove the recognition capabilities of this neural architecture.
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