Bob’s Basic Image Processing Routines

This Python module contains base functionality from Bob bound to Python, available in the C++ counter-part bob::ip::base.

References

[Atanasoaei2012]Cosmin Atanasoaei. Multivariate Boosting with Look-up Tables for Face Processing, PhD thesis, EPFL, 2012. pdf
[Sanderson2002]Conrad Sanderson and Kuldip K. Paliwal. Polynomial Features for Robust Face Authentication, In Proceedings of the IEEE International Conference on Image Processing, 2002. pdf
[TanTriggs2007]Xiaoyang Tan and Bill Triggs. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions, In International Conference on Analysis and Modeling of Faces and Gestures, 2007. pdf
[Jobson1997]D. Jobson, Z. Rahman and G. Woodell. A Multiscale Retinex for bridging the gap between color images and the Human observation of scenes, In IEEE Transactions on Image Processing, vol. 6, n. 7, 1997.
[Wang2004]H. Wang, S.Z. Li and Y. Wang. Face Recognition under Varying Lighting Conditions Using Self Quotient Image, In IEEE International Conference on Image Processing, vol. 2, pp. 1397-1400, 2004.
[Lowe2004]D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, In International Journal of Computer Vision, 2004.
[Dalal2005]N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005.
[Haralick1973]R. M. Haralick, K. Shanmugam, I. Dinstein. Textural Features for Image Classification, In IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-3, No. 6, p. 610-621, 1973.
[Szeliski2010]Richard Szeliski. Computer Vision: Algorithms and Applications (1st ed.). Springer-Verlag New York, USA, 2010.

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