Computer Vision: Algorithms and Applications
Springer Science & Business Media, 30 sept. 2010 - 812 pages
Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques
Topics and features:
Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
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Chapter 3 Image processing
Chapter 4 Feature detection and matching
Chapter 5 Segmentation
Chapter 6 Featurebased alignment
Chapter 7 Structure from motion
Chapter 8 Dense motion estimation
Chapter 9 Image stitching
Chapter 12 3D reconstruction
Chapter 13 Imagebased rendering
Chapter 14 Recognition
Chapter 15 Conclusion
Appendix A Linear algebra and numerical techniques
Appendix B Bayesian modeling and inference
Appendix C Supplementary material