Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size |
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| Affiliation: | 1. Center on Mental Health Services Research and Policy, Department of Psychiatry, University of Illinois at Chicago, 1601 West Taylor Street, M/C 912, Chicago, IL 60612, United States;2. Collaborative Support Programs of New Jersey, Rutgers, The State University of New Jersey, Department of Psychiatric Rehabilitation and Counseling Professions, 8 Spring Street, Freehold, NJ 07728, United States;3. Georgia Regents University, Department of Psychiatry and Health Behavior, 1120, 15th Street, Augusta, GA 30912, United States;1. Department of Computer Engineering, German Jordanian University, P.O. Box 35247, Amman 11180, Jordan;2. Department of Network Engineering and Security, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan |
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| Abstract: | In this paper we demonstrate how to use statistical evaluation for texture recognition in the case of window-size of the imaging focal-plane sensor being smaller than the pattern of the texture. The evaluation method is similar to the sub-pixel pattern recognition developed by the first author. We have reported in an earlier publication on the development of a new single-chip texture classifier smart-sensor system, whose main part is a cellular nonlinear network (CNN) VLSI chip. This architecture is very fast but it has a limited window-size. Now we show that this architecture can effectively recognize textures of periodicity larger than the window-size. As a result, we recognized 15 Brodatz-textures by using a 20 × 22 CNN chip with a 0.4% error-rate. |
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