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

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Cosmin Atanasoaei. Multivariate Boosting with Look-up Tables for Face Processing, PhD thesis, EPFL, 2012. pdf

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Conrad Sanderson and Kuldip K. Paliwal. Polynomial Features for Robust Face Authentication, In Proceedings of the IEEE International Conference on Image Processing, 2002. pdf

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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

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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.

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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.

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D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, In International Journal of Computer Vision, 2004.

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N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005.

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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.

Zhao2007

G. Zhao and M. Pietikainen. Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 915-928, June 2007. doi: 10.1109/TPAMI.2007.1110

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