CNN computer for high-speed visual inspection
Rodrigo Montúfar‐Chaveznava, Domingo Guinea, M. C. García‐Alegre, Víctor M. Preciado · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
An image entails a huge amount of data and information. For this reason, image synthesis and analysis by computer systems requires a high processing time. This represents a handicap in systems where real time processing or an immediate interpretation is demanded as in visual inspection industrial applications. Present work, introduces a computer architecture for the construction of a compact real-time system for high speed visual inspection. The vision system is essentially a Cellular Neural Network Computer (CNN-C) basically composed of a Cellular Neural Network Universal Machine (CNN-UM), an analog memory, an imager and a control unit with mixed-signal properties. This prototype has some limitations, but represents the first approximation of a new kind of systems for visual inspection. The CNN-C prototype will be tested in visual inspection of paper, metal and polymer surfaces. Besides the CNN-C can be used in many other image processing tasks, such as coding, singularity detection or multiresolution representation.