References
- Ballard, D. 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13(2), pp. 111–122.
- Baggio, D. L. 2012. Mastering OpenCV with Practical Computer Vision Projects. PACKT Publishing.
- Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L. 2008. Speeded Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), pp. 346–359.
- Borgefors, G. 1988. Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6), pp. 849–865.
- Bouguet, J.-Y. 2000. Pyramidal Implementation of the Lucas Kanade Feature Tracker - Description of the Algorithm, Intel Corporation.
- Brostow, G. J., Fauqueura, J. and Cipolla, R. 2009. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, 30, pp. 88–97.
- Brunelli, R. 2009. Template Matching Techniques in Computer Vision: Theory and Practice. Wiley & Sons, Inc.
- Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), pp. 679–698.
- Chen, F., Delanney, D., and De Vleeschouwer, C. 2011. An autonomous framework to produce and distribute personalized team-sport video summaries: a basketball case study. IEEE Transactions on Multimedia, 13(6), pp. 1381–1394.
- Cristianini, N. and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press.
- Cyganek, B. ...
Get A Practical Introduction to Computer Vision with OpenCV now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.