## Robot visionThis 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. 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. Berthold Klaus Paul Horn is Associate Professor, Department of Electrical Engineering and Computer Science, MIT. Robot Vision is included in the MIT Electrical Engineering and Computer Science Series. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Introduction | 1 |

Image Formation Image Sensing | 18 |

Geometrical Properties | 46 |

Copyright | |

18 other sections not shown

### Common terms and phrases

angle binary image boundary BRDF brightness gradient calculus of variations camera chapter components computed consider constraint convex convolution coordinate system corresponding curve defined determine differential direction discrete discussed distance distribution edge edge detection equations estimate Euler number example extended Gaussian image figure filter Fourier transform frequency Gaussian curvature Gaussian sphere given gradient space gray-level Hint histogram image brightness image irradiance image plane image processing integral inverse iterative labeling Laplacian lens light sources linear machine vision matching matrix method minimize motion field needle diagram neighbors noise object obtained occluding oo roo operator optical axis optical flow orientation histogram parallel parameters partial derivatives photometric stereo picture cells point-spread function problem projection quaternion radiance recover reflectance map region result rotation rotationally symmetric sampling sensor shape shift-invariant Show smooth solid of revolution solution spatial Suppose surface normal surface orientation surface patch tessellation values vector vision system zero