Computer and Machine Vision
Theory, Algorithms, PracticalitiesBy
- 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.
Key features include:
- Practical examples and case studies give the âins and outsâ 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 ârecent developmentsâ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject.
Embedded, electronic systems, signal/image processing and computer engineering R&D engineers; post graduates and PhD researchers in machine and computer vision.
Hardbound, 912 Pages
Published: March 2012
Imprint: Academic Press
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
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
20 Automated Visual Inspection
21 Inspection of Cereal Grains
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