Authors: Mokhtar Beldjehem
Addresses: Sainte-Anne's University, 1589 Walnut Street, Halifax, Nova Scotia, B3H 3S1, Canada
Abstract: We propose a novel unifying framework for building a novel granular modular architecture for a machine visual system that accommodates a large spectrum of potential vision problems. Thus removing the ad hoc nature of present solutions and providing the basis for new generation of machine visual systems. Such a framework works by integrating some useful concepts from the human vision processes and adding some interesting granular functionalities of human cognition and it advocates further hybridisation of non-linear digital filters and soft computing in implementing such machine intelligent visual systems. Our focus herein will be on the low level and mid-level stages of such a framework. The goal is to build an automatic system that can be used for degraded multi-modal image processing, including x-rays, MRI, Sonar, etc. for diagnosis, recognition, registration and information fusion purposes. For illustration purposes, an investigation concerning its application to a real world problem is also provided. We are interested by an application to automatic detection and classification of patients| spines affected by idiopathic scoliosis from X-rays images.
Keywords: perception engineering; modular granular vision architecture; visual front-end module; mid-level module; fuzzy sets; fuzzy logic; possibility theory; hybrid fuzzy-neuro models; granulation; nonlinear digital filters; x-rays images; neural networks; degraded images; multi-modal image processing; automatic detection; automatic classification; spine images; idiopathic scoliosis; medical imaging.
International Journal of Advanced Intelligence Paradigms, 2011 Vol.3 No.3/4, pp.189 - 202
Published online: 26 Mar 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article