Bob’s Basic Image Processing Routines¶
Todo
Explain DCTFeatures constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.DCTFeatures, line 14.)
Todo
The parameter(s) ‘levels, max_level, min_level, quantization_table’ are used, but not documented.
Parameters:
dtype
: numpy.dtype
[default:numpy.uint8
] The data-type for the GLCM class
glcm
: bob.ip.base.GLCM
The GLCM object to use for copy-construction
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.GLCM, line 19.)
Todo
Explain GaussianScaleSpace constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.GaussianScaleSpace, line 13.)
Todo
UPDATE as this is not true
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.LBPTop, line 7.)
Todo
Add documentation for MultiscaleRetinex
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.MultiscaleRetinex, line 13.)
Todo
Check if this documentation is correct (seems to be
copied from bob.ip.base.SelfQuotientImage
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.MultiscaleRetinex.process, line 4.)
Todo
Explain SIFT constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.SIFT, line 13.)
Todo
explain SelfQuotientImage constructor
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.SelfQuotientImage, line 15.)
Todo
Explain TanTriggs constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.TanTriggs, line 13.)
Todo
Explain VLDSIFT constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.VLDSIFT, line 12.)
Todo
Describe the output of the VLDSIFT.extract()
method in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.VLDSIFT.extract, line 8.)
Todo
Explain VLSIFT constructor in more detail.
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.VLSIFT, line 12.)
Todo
explain WeightedGaussian constructor
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.WeightedGaussian, line 13.)
Todo
Explain gamma correction in more detail
(The original entry is located in /home/travis/build/bioidiap/bob.ip.base/bob/ip/base/__init__.py:docstring of bob.ip.base.gamma_correction, line 3.)
This Python module contains base functionality from Bob bound to Python, available in the C++ counter-part bob::ip::base
.
Documentation¶
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. |