Computer and Machine Vision: Theory, Algorithms, Practicalities

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 ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practiceNew chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer visionNecessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examplesUpdated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imagingThe ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject

Mathematics and essential theory are made approachable by careful explanations and well-illustrated examplesUpdated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imagingThe ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the 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