Gabor filter

[1] The Gabor filter was first generalized to 2D by Gösta Granlund,[2] by adding a reference direction.

The Gabor filter is a linear filter used for texture analysis, which essentially means that it analyzes whether there is any specific frequency content in the image in specific directions in a localized region around the point or region of analysis.

Frequency and orientation representations of Gabor filters are claimed by many contemporary vision scientists to be similar to those of the human visual system.

Some authors claim that simple cells in the visual cortex of mammalian brains can be modeled by Gabor functions.

[4][5] Thus, image analysis with Gabor filters is thought by some to be similar to perception in the human visual system.

[8] Jones and Palmer showed that the real part of the complex Gabor function is a good fit to the receptive field weight functions found in simple cells in a cat's striate cortex.

In this way, time-frequency analysis based on the resulting complex-valued extension of the time-causal limit kernel makes it possible to capture essentially similar transformations of a temporal signal as the Gabor filter can, and as can be described by the Heisenberg group, see [10] for further details.

A set of Gabor filters with different frequencies and orientations may be helpful for extracting useful features from an image.

2D Gabor filters have rich applications in image processing, especially in feature extraction for texture analysis and segmentation.

[13] Gabor filters with different frequencies and with orientations in different directions have been used to localize and extract text-only regions from complex document images (both gray and colour), since text is rich in high frequency components, whereas pictures are relatively smooth in nature.

[14][15][16] It has also been applied for facial expression recognition [17] Gabor filters have also been widely used in pattern analysis applications.

For example, it has been used to study the directionality distribution inside the porous spongy trabecular bone in the spine.

Furthermore, important activations can be extracted from the Gabor space in order to create a sparse object representation.

This is an example implementation in MATLAB/Octave: Code for Gabor feature extraction from images in MATLAB can be found at http://www.mathworks.com/matlabcentral/fileexchange/44630.