Computer and Machine Vision: Theory, Algorithms, Practicalities, Fifth Edition clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory, while also emphasizing algorithmic and practical design constraints. This fully revised 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 matter. New to this edition: Two new chapters on shape/appearance-based object detection, including face detection and recognition, three new chapters on Machine Learning, new sections on geometric transformations, histogram of oriented gradients (HOG), Gabor filters, homographies, multiview geometry, wide baseline matching using SIFT, SURF and HOG. In addition, the book includes a website with accompanying MATLAB algorithms and transparencies with tasks and projects. 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 practiceNecessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examplesThe 'recent developments' section included in each chapter will be useful in bringing students and practitioners up to date with the subject INDICE: 1. Vision, the Challenge 2. Images and Imaging Operations 3. Machine Learning 1: Basic Classification Concepts Part 1 Low-Level Vision 4. Image Filtering and Morphology 5. Segmentation 1: the Role of Thresholding 6. Segmentation 2: Edge Detection 7. Segmentation 3: Corner, Interest Point and Invariant Feature Detection 8. Segmentation 4: Texture Analysis Part 2 Intermediate-Level Vision 9. Binary Shape Analysis 10. Boundary Pattern Analysis 11. Model-based Vision 1: Line, Circle and Ellipse Detection 12. Model-based Vision 2: The Value of the Generalised Hough Transform 13. Machine Learning 2: Probabilistic Methods 14. Shape and Appearance Models Part 3 3-D Vision and Motion 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. Machine Learning 3: Temporal Models and Tracking Part 4 Towards Complete Pattern Recognition Systems 21. Face Detection and Recognition 22. Surveillance 23. In-Vehicle Vision Systems 24. Epilogue - Perspectives in Vision Appendix A Robust statistics Appendix B The Sampling Theorem
- ISBN: 978-0-12-809284-2
- Editorial: Academic Press
- Encuadernacion: Cartoné
- Páginas: 912
- Fecha Publicación: 01/10/2017
- Nº Volúmenes: 1
- Idioma: Inglés