About the Author
Foreword
Preface to the Fifth Edition
Preface to the First Edition
Acknowledgments
Topics Covered in Application Case Studies
Glossary of Acronyms and Abbreviations
CHAPTER 1 Vision, the challenge
PART 1 LOW-LEVEL VISION
CHAPTER 2 Images and imaging operations
CHAPTER 3 Image filtering and morphology
CHAPTER 4 The role of thresholding
CHAPTER 5 Edge detection
CHAPTER 6 Corner, interest point, and invariant feature detection
CHAPTER 7 Texture analysis
PART 2 IMTERMEDIATE-LEVEL VISION
CHAPTER 8 Binary shape analysis
CHAPTER 9 Boundary pattern analysis
CHAPTER 10 Line, circle, and ellipse detection
CHAPTER 11 The generalized Hough transform
CHAPTER 12 Object segmentation and shape models
PART 3 MACHINE LEARNING AND DEEP LEARNING NETWORKS
CHAPTER 13 Basic classification concepts
CHAPTER 14 Machine learning: provavilistic methods
CHAPTER 15 Deep-learning networks
PART 4 3D VISION AND MOTION
CHAPTER 16 The three-dimensional world
CHAPTER 17 Tackling the perspective n-po...int problem
CHAPTER 18 Invariants and perspective
CHAPTER 19 Image transformations and camera calibration
CHAPTER 20 Motion
PART 5 PUTTING COMPUTER VISION TO WORK
CHAPTER 21 Face detection and recognition: the impact of deep learning
CHAPTER 22 Surveillance
CHAPTER 23 In-vehicle vision systems
CHAPTER 24 Epilogue-Perspectives in vision
Appendix A: Robust statistics
Appendix B: The sampling theorem
Appendix C: The representation of color
Appendix D: Sampling from distributions
References
Index