Computer Vision: Principles, Algorithms, Applications, Learning (Hardcover, 5)

1. Vision, the Challenge 2. Images and Imaging Operations 3. Image Filtering and Morphology 4. The Role of Thresholding 5. Edge Detection 6. Corner, Interest Point and Invariant Feature Detection 7. Texture Analysis 8. Binary Shape Analysis 9. Boundary Pattern Analysis 10. Line, Circle and Ellipse Detection 11. The Generalised Hough Transform 12. Object Segmentation and Shape Models 13. Basic Classification Concepts 14. Machine Learning: Probabilistic Methods 15. Deep Learning Networks 16. The Three-Dimensional World 17. Tackling the Perspective n-point Problem 18. Invariants and perspective 19. Image transformations and camera calibration 20. Motion 21. Face Detection and Recognition: the Impact of Deep Learning 22. Surveillance 23. In-Vehicle Vision Systems 24. Epilogue?Perspectives in Vision Appendix A: Robust statistics Appendix B: The Sampling Theorem Appendix C: The representation of colour Appendix D: Sampling from distributions