Computer Vision: Algorithms and Applications/
by Richard Szeliski
- 2nd ed.
- Switzerland: Springer, 2022.
- xxii, 925 p.: ill.; 29 cm.
includes index & bibliography
1. Introduction 2. Image formation 3. Image processing 4. Model fitting and optimization 5. Deep learning 6. Recognition 7. Feature detection and matching 8. Image alignment and stitching 9. Motion estimation 10. Computational photography 11. Structure from motion and SLAM 12. Depth estimation 13. 3D reconstruction 14. Image-based rendering Conclusion Appendix A: Linear algebra Appendix B: Bayesian modeling and inference Appendix C: Supplementary material
Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.