Computer and Machine Vision

Presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This text contains case studies on surveillance and driver assistance systems that give practical methods on these advanced applications in computer vision.

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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. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject.

Key features include:



Practical examples and case studies give the ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practice
New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision
Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject