Next: Generating the feature image
Up: Document Segmentation
Previous: Gaussian and Laplacian Pyramids
Contents
The Gabor Filters have received considerable attention because the characteristics of certain cells in the visual cortex of some mammals can be approximated by these filters. In addition these filters have been shown to posses optimal localization properties in both spatial and frequency domain and thus are well suited for texture segmentation problems [13,20].
Gabor filters have been used in many applications, such as texture segmentation, target detection, fractal dimension management, document analysis, edge detection, retina identification, image coding and image representation [21].
A Gabor filter can be viewed as a sinusoidal plane of particular frequency and orientation, modulated by a Gaussian envelope. It can be written as:

(11) 
where s(x,y) is a complex sinusoid, known as a carrier, and is a 2D Gaussian shaped function, known as envelope. The complex sinusoid is defined as follows,

(12) 
The 2D Gaussian function is defined as follows,

(13) 
Thus the 2D Gabor filter can be written as:

(14) 
The frequency response of the filter is:

(15) 
=
where,
This is equivalent to translating the Gaussian function by in the frequency domain. Thus the Gabor function can be thought of as being a Gaussian function shifted in frequency to position i.e at a distance of
from the origin and at an orientation of
.
In the above 2 equations, (,) are referred to as the Gabor filter spatial central frequency. The parameters
are the standard deviation of the Gaussian envelope along X and Y directions and determine the filter bandwidth.
Figure 6:
Plot of frequency response of the Gabor filter for different values of u0, v0 corresponding to four orientations  0, 45, 90 and 135

A plot of the frequency response of the Gabor filter for different values of u0, v0 corresponding to four orientations  0, 45, 90 and 135 is shown in Figure 6.
Figure 7:
Gabor filter output of the simple lines pattern corresponding to 4 orientations  0, 45, 90 and 135

Figure 7 shows the output of the Gabor filter at four orientations, when the input is an image containing lines at similar frequencies but at different angles. It can be seen that the filter at orientation has a strong response to the region when the variation is also along angle . In this case, a simple smoothing operation followed by thresholding is enough to segment the image into four regions corresponding to the lines at four orientations.
By passing an image through a Gabor filter defined by the parameters (
, we obtain all those components in the image that have their energies concentrated near the spatial frequency point .
Next: Generating the feature image
Up: Document Segmentation
Previous: Gaussian and Laplacian Pyramids
Contents
20020603