CNN based visual processing for industrial inspection

Domingo Guinea; Victor M. Preciado; Jose Vicente; Angela Ribeiro; Maria C. Garcia-Alegre

doi:10.1117/12.380086

21 March 2000 CNN based visual processing for industrial inspection

Domingo Guinea, Victor M. Preciado, Jose Vicente, Angela Ribeiro, Maria C. Garcia-Alegre

Proceedings Volume 3966, Machine Vision Applications in Industrial Inspection VIII; (2000) https://doi.org/10.1117/12.380086
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

Many industrial inspection such visual quality control of metal laminates require billions of operations per second. Analog CNN array computer arises as an alternative to traditional digital processors, capable of make in a single chip Tera equivalent operations per second. A 4096 analog CNN processor array is able to perform complex space-time image analysis, being much faster than a camera-computer system in continuous inspection applications. Both chips have been implemented in CMOS technology and they are managed by a 32- bit high-performance low-cost micro-controller that closes the pan, tilt, lighting, focus and zoom loops required in the implementation of the active vision strategies. Several convolution masks for the Cellular Processors has been selected to detect particular changes in the texture, size, direction or orientation of the image entities, reprogramming 'on the fly' the pixel resolution or shape when necessary. Laboratory results present these Cellular Processors and multiple resolution imager circuits as a promising architecture for visual inspection of industrial processes in real time.

Citation Download Citation

Domingo Guinea, Victor M. Preciado, Jose Vicente, Angela Ribeiro, and Maria C. Garcia-Alegre "CNN based visual processing for industrial inspection", Proc. SPIE 3966, Machine Vision Applications in Industrial Inspection VIII, (21 March 2000); https://doi.org/10.1117/12.380086

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