Computer Vision: Algorithms and ApplicationsSpringer 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. |
Table des matières
27 | |
Chapter 3 Image processing | 87 |
Chapter 4 Feature detection and matching | 181 |
Chapter 5 Segmentation | 235 |
Chapter 6 Featurebased alignment | 272 |
Chapter 7 Structure from motion | 303 |
Chapter 8 Dense motion estimation | 335 |
Chapter 9 Image stitching | 375 |
Chapter 12 3D reconstruction | 504 |
Chapter 13 Imagebased rendering | 543 |
Chapter 14 Recognition | 574 |
Chapter 15 Conclusion | 641 |
Appendix A Linear algebra and numerical techniques | 645 |
Appendix B Bayesian modeling and inference | 661 |
Appendix C Supplementary material | 679 |
691 | |
Chapter 10 Computational photography | 409 |
Chapter 11 Stereo correspondence | 467 |