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4 April 2001 CNN computer for high-speed visual inspection
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Abstract
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rodrigo Montufar-Chaveznava, Domingo Guinea, Maria C. Garcia-Alegre, and Victor M. Preciado "CNN computer for high-speed visual inspection", Proc. SPIE 4301, Machine Vision Applications in Industrial Inspection IX, (4 April 2001); https://doi.org/10.1117/12.420917
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