e.r.davies

　　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.

1 Vision, the Challenge
2 Images and Imaging Operations
3 Basic Image Filtering Operations
4 Thresholding Techniques
5 Edge Detection
6 Corner and Interest Point Detection
7 Mathematical Morphology
8 Texture
9 Binary Shape Analysis
10 Boundary Pattern Analysis
11 Line Detection
12 Circle and Ellipse Detection
13 The Hough Transform and Its Nature
14 Abstract Pattern Matching Techniques
15 The Three-Dimensional World
16 Tackling the perspective n-point problem
17 Invariants and perspective
18 Image transformations and camera calibration
19 Motion
20 Automated Visual Inspection
21 Inspection of Cereal Grains
22 Surveillance
23 In-Vehicle Vision Systems24 Statistical Pattern Recognition
25 Image Acquisition
26 Real-Time Hardware and Systems Design Considerations
27 Epilogue-Perspectives in Vision
Appendix Robust statistics
References
Author Index
Subject Index