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.