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Titlebook: Intelligent Visual Inspection; Using artificial neu Ryan G. Rosandich Book 1997 Ryan G. Rosendich 1997 artificial neural network.artificial

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樓主: fallacy
31#
發(fā)表于 2025-3-27 00:20:27 | 只看該作者
Significantly this book includes: * broadcoverage of all aspects ofthe automated visual inspection problem, *details of the HAVENETneural network and the CAMERA vision model,and * detaileddescriptions of practical applications of intelligentvisualinspection.978-1-4612-8510-6978-1-4613-1201-7
32#
發(fā)表于 2025-3-27 03:03:50 | 只看該作者
33#
發(fā)表于 2025-3-27 08:10:03 | 只看該作者
34#
發(fā)表于 2025-3-27 11:17:06 | 只看該作者
Automated visual inspection systemsstems proposed to solve these problems are similar in that they are automated, they are designed to improve on human inspection and on commercially available inspection systems, and they are capable of learning defect categories from examples.
35#
發(fā)表于 2025-3-27 13:54:05 | 只看該作者
Book 1997re, many of the techniques developedthrough theseefforts are now mature enough for use in practicalapplications.Automated Visual Inspection Using Artificial NeuralNetworks explainsthe application of recently emerging technology inthe areas ofartificial vision and neural networks to automatedvisual i
36#
發(fā)表于 2025-3-27 18:33:18 | 只看該作者
Artificial neural networks for pattern recognitione the receiving neuron into greater activity, while others tend to inhibit its activity. The human nervous system consists of approximately 10. neurons, massively interconnected by a total of approximately 10. synapses, with some connections extending over a meter in length (Wasserman, 1989).
37#
發(fā)表于 2025-3-28 01:13:52 | 只看該作者
Image acquisition and storageculty that will be encountered in further processing, and many challenging image processing problems can be eliminated or at least minimized by careful detailed design of the image acquisition system.
38#
發(fā)表于 2025-3-28 03:21:56 | 只看該作者
39#
發(fā)表于 2025-3-28 07:28:33 | 只看該作者
40#
發(fā)表于 2025-3-28 13:09:20 | 只看該作者
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