Gabor Image Features

Posted on October 30, 2012

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Computation of Gabor Features – Mean Squared Energy, Mean Amplitude. In applications of computer vision and image analysis, Gabor filters have maintained their popularity in feature extraction for almost three decades. The original reason that draw attention was the similarity between Gabor filters and the receptive field of simple cells in the visual cortex. A more practical reason is their success in many applications, e.g., face detection and recognition, iris recognition and fingerprint matching, where Gabor feature based methods are among the top performers. The derivation of Gabor features is elegant through the fundamental domains of signal processing: space (time) and frequency. Topped with many practical and computational advantages. To know more about Gabor Features, I would suggest reading Kamarainen, Joni-Kristian. “Gabor features in image analysis.” Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on. IEEE, 2012. [HERE]

Download MatLab Code :  http://www.mathworks.com/matlabcentral/fileexchange/38844-gabor-image-features:

References:
Kamarainen, Joni-Kristian. “Gabor features in image analysis.” Image Processing
Theory, Tools and Applications (IPTA), 2012 3rd International Conference on
. IEEE, 2012. [HERE]

Peter Kovesi, “Symmetry and Asymmetry From Local Phase” AI’97, Tenth 
Australian Joint Conference on Artificial Intelligence. 2 – 4 December 
1997. http://www.cs.uwa.edu.au/pub/robvis/papers/pk/ai97.ps.gz

Peter Kovesi, “Image Features From Phase Congruency”. Videre: A 
Journal of Computer Vision Research. MIT Press. Volume 1, Number 3, 
Summer 1999 http://mitpress.mit.edu/e-journals/Videre/001/v13.html 

Kuse, Manohar, Yi-Fang Wang, Vinay Kalasannavar, Michael Khan, and Nasir Rajpoot. 
“Local isotropic phase symmetry measure for detection of beta cells and lymphocytes.” 
Journal of Pathology Informatics 2 (2011). 

Naik, S., et al. Automated gland and nuclei segmentation for grading of prostate and breast 
cancer histopathology. in IEEE International Symposium on Biomedical Imaging (ISBI). 2008. 
IEEE. 

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