A self-learning camera for the validation of highly variable and pseudo-random patterns

Michael Kelley

doi:10.1117/12.527149

3 May 2004 A self-learning camera for the validation of highly variable and pseudorandom patterns

Michael Kelley

Proceedings Volume 5303, Machine Vision Applications in Industrial Inspection XII; (2004) https://doi.org/10.1117/12.527149
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Reliable and productive manufacturing operations have depended on people to quickly detect and solve problems whenever they appear. Over the last 20 years, more and more manufacturing operations have embraced machine vision systems to increase productivity, reliability and cost-effectiveness, including reducing the number of human operators required. Although machine vision technology has long been capable of solving simple problems, it has still not been broadly implemented. The reason is that until now, no machine vision system has been designed to meet the unique demands of complicated pattern recognition. The ZiCAM family was specifically developed to be the first practical hardware to meet these needs. To be able to address non-traditional applications, the machine vision industry must include smart camera technology that meets its users’ demands for lower costs, better performance and the ability to address applications of irregular lighting, patterns and color. The next-generation smart cameras will need to evolve as a fundamentally different kind of sensor, with new technology that behaves like a human but performs like a computer. Neural network based systems, coupled with self-taught, n-space, non-linear modeling, promises to be the enabler of the next generation of machine vision equipment. Image processing technology is now available that enables a system to match an operator’s subjectivity. A Zero-Instruction-Set-Computer (ZISC) powered smart camera allows high-speed fuzzy-logic processing, without the need for computer programming. This can address applications of validating highly variable and pseudo-random patterns. A hardware-based implementation of a neural network, Zero-Instruction-Set-Computer, enables a vision system to “think” and “inspect” like a human, with the speed and reliability of a machine.

Citation Download Citation

Michael Kelley "A self-learning camera for the validation of highly variable and pseudorandom patterns", Proc. SPIE 5303, Machine Vision Applications in Industrial Inspection XII, (3 May 2004); https://doi.org/10.1117/12.527149

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