Computer and Machine Vision: Theory, Algorithms, PracticalitiesComputer 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:
|
Contents
| 1 | |
| 15 | |
2 IntermediateLevel Vision | 227 |
3 3D Vision and Motion | 387 |
4 Toward RealTime Pattern Recognition Systems | 523 |
Appendix A Robust Statistics | 778 |
References | 796 |
Author Index | 845 |
Subject Index | 861 |
Color Plates | 872 |
Other editions - View all
Common terms and phrases
accuracy achieved algorithm analysis applied approach arise background basic binary boundary camera Chapter color components Computer Vision consider corner corner detector cross-ratio curve Davies detector distance distribution edge detection edge points effect eliminate ellipse error estimate fact FIGURE function Gaussian give gradient graph grayscale Hence histogram Hough transform IEEE IEEE Trans Image Process implementation important inspection intensity invariant Kalman filter Lett Machine Intell Machine Vision masks matching matrix maximal clique means measure median filter method motion neighborhood noise normal Note objects obtained occlusion operator optical flow optimal orientation original image overall parameter space particle filter Pattern Anal Pattern Recogn peak perspective projection pixels plane position possible problem RANSAC real-time recognition region relevant result robust rotation sampling scene Section segmentation shape shows signal situation solution stereo symmetry technique template texture thresholding tion tracking vector vehicles