Computer and Machine Vision. Theory, Algorithms,Machine vision MV is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control , and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision , a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environments such as security and vehicle guidance.
[PDF] Machine Vision, Third Edition: Theory, Algorithms, Practicalities (Signal Processing and its
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Computer and Machine Vision: Theory, Algorithms, Practicalities previously entitled Machine Vision clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. He has worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests include automated visual inspection, surveillance, vehicle guidance, crime detection and neural networks. He has published more than papers, and three books. Machine Vision: Theory, Algorithms, Practicalities has been widely used internationally for more than 25 years, and is now out in this much enhanced fifth edition.
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April , Cite as. A machine vision system that has been developed for the detection of missing fasteners on steel stampings is described. The system has been tested using images generated by a commercial machine vision system installed on an assembly line for the production of automotive cross-car beams. This particular application was considered to be challenging due to variations in the operating conditions, such as the lighting, together with the fact that the appearance of the fasteners could change depending upon their angle relative to the camera. A neuro-fuzzy image classification algorithm was developed and tested against a threshold-based classification algorithm. Results indicate that both algorithms perform well when optimized, but the neuro-fuzzy algorithm was found to degrade in a less abrupt fashion when the input data deviated from the trained data.