Fundamentals of Computer Vision

This book equips students with crucial mathematical and algorithmic tools to understand complete computer vision systems.

Wesley E. Snyder (Author), Hairong Qi (Author)

9781107184886, Cambridge University Press

Hardback, published 28 September 2017

390 pages
26 x 18.2 x 2.2 cm, 0.96 kg

'Written with depth and clarity, this book introduces a variety of fundamentals needed to participate in computer vision research. Not only does it focus on mathematical fundamentals, but it shows all with well-motivated, concrete examples and applications. I believe vision students and researchers will find this book valuable. I look forward to teaching from it.' Tianfu Wu, North Carolina State University

Computer vision has widespread and growing application including robotics, autonomous vehicles, medical imaging and diagnosis, surveillance, video analysis, and even tracking for sports analysis. This book equips the reader with crucial mathematical and algorithmic tools to develop a thorough understanding of the underlying components of any complete computer vision system and to design such systems. These components include identifying local features such as corners or edges in the presence of noise, edge preserving smoothing, connected component labeling, stereopsis, thresholding, clustering, segmentation, and describing and matching both shapes and scenes. The extensive examples include photographs of faces, cartoons, animal footprints, and angiograms, and each chapter concludes with homework exercises and suggested projects. Intended for advanced undergraduate and beginning graduate students, the text will also be of use to practitioners and researchers in a range of applications.

Part I. Preliminaries: 1. Computer vision, some definitions, and some history
2. Writing programs to process images
3. Review of mathematical principles
4. Images – representation and creation
Part II. Preprocessing: 5. Kernel operators
6. Noise removal
7. Mathematical morphology
Part III. Image Understanding: 8. Segmentation
9. Parametric transforms
10. Representing and matching shape
11. Representing and matching scenes
Part IV. The 2-D Image in a 3-D World: 12. Relating to three dimensions
13. Developing computer vision algorithms.

Subject Areas: Image processing [UYT], Computer vision [UYQV]