Bob’s Basic Image Processing Routines¶
Todo
Explain DCTFeatures constructor in more detail.
(The original entry is located in /scratch/builds/bob/bob.ip.base/miniconda/conda-bld/bob.ip.base_1635539312079/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_place/lib/python3.8/site-packages/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 /scratch/builds/bob/bob.ip.base/miniconda/conda-bld/bob.ip.base_1635539312079/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_place/lib/python3.8/site-packages/bob/ip/base/__glcm__.py:docstring of bob.ip.base.__glcm__.GLCM, line 19.)
Todo
UPDATE as this is not true
(The original entry is located in /scratch/builds/bob/bob.ip.base/miniconda/conda-bld/bob.ip.base_1635539312079/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_place/lib/python3.8/site-packages/bob/ip/base/__init__.py:docstring of bob.ip.base.LBPTop, line 7.)
Todo
Explain TanTriggs constructor in more detail.
(The original entry is located in /scratch/builds/bob/bob.ip.base/miniconda/conda-bld/bob.ip.base_1635539312079/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_place/lib/python3.8/site-packages/bob/ip/base/__init__.py:docstring of bob.ip.base.TanTriggs, line 13.)
Todo
explain WeightedGaussian constructor
(The original entry is located in /scratch/builds/bob/bob.ip.base/miniconda/conda-bld/bob.ip.base_1635539312079/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_place/lib/python3.8/site-packages/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 /scratch/builds/bob/bob.ip.base/miniconda/conda-bld/bob.ip.base_1635539312079/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_place/lib/python3.8/site-packages/bob/ip/base/__init__.py:docstring of bob.ip.base._library.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
- 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.
- 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