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This book presents a coherent approach to the fast moving field of machine vision, using a consistent notation based on a detailed understanding of the image formation process. It covers even the most recent research and will provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.An outgrowth of the author's course at MIT, Robot Vision presents a solid framework for understanding existing work and planning future research.
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Its coverage includes a great deal of material that important to engineers applying machine vision methods in the real world. The chapters on binary image processing, for example, help explain and suggest how to improve the many commercial devices now available. And the material on photometric stereo and the extended Gaussian image points the way to what may be the next thrust in commercialization of the results in this area. The many exercises complement and extend the material in the text, and an extensive bibliography will serve as a useful guide to current research. Contents Image Formation and Image Sensing Binary Images: Geometrical Properties; Topological Properties Regions and Image Segmentation Image Processing: Continuous Images; Discrete Images Edges and Edge Finding Lightness and Color Reflectance Map: Photometric Stereo Reflectance Map; Shape from Shading Motion Field and Optical Flow Photogrammetry and Stereo Pattern Classification Polyhedral Objects Extended Gaussian Images Passive Navigation and Structure from Motion Picking Parts out of a Bin. Horn, a leading researcher in the area of human and machine vision for many years, has written an excellent textbook on the subject, which is emminently accessible to engineers, teachers, and scientists working in the vision area.
The book follows a rigorous mathematical framework, beginning with the physics of image formation, and drawing on the most recent computational theories of human/machine perception of lightness, shape, movement, and depth, concluding with chapters devoted to realistic applications in automated navigation and industrial robotics.” — Al Bovik, Department of Electrical and Computer Engineering, University of Texas at Austin.