Programming Computer Vision with Python

Appendix D. References

[1]	Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. SURF: Speeded up robust features. In European Conference on Computer Vision, 2006.
[2]	Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:2001, 2001.
[3]	Gary Bradski and Adrian Kaehler. Learning OpenCV. O’Reilly Media Inc., 2008.
[4]	Martin Byröd. An optical Sudoku solver. In Swedish Symposium on Image Analysis, SSBA. http://www.maths.lth.se/matematiklth/personal/byrod/papers/sudokuocr.pdf, 2007.
[5]	Antonin Chambolle. Total variation minimization and a class of binary mrf models. In Energy Minimization Methods in Computer Vision and Pattern Recognition, Lecture Notes in Computer Science, pages 136–152. Springer Berlin / Heidelberg, 2005.
[6]	T. Chan and L. Vese. Active contours without edges. IEEE Trans. Image Processing, 10(2):266–277, 2001.
[7]	Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[8]	D. Cremers, T. Pock, K. Kolev, and A. Chambolle. Convex relaxation techniques for segmentation, stereo and multiview reconstruction. In Advances in Markov Random Fields for Vision and Image Processing. MIT Press, 2011.
[9]	Nello Cristianini and John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
[10]	Gunnar Farnebäck. Two-frame motion ...