Paper
28 May 2004 Optimized multichannel decomposition for texture segmentation using Gabor filter bank
Author Affiliations +
Proceedings Volume 5298, Image Processing: Algorithms and Systems III; (2004) https://doi.org/10.1117/12.526300
Event: Electronic Imaging 2004, 2004, San Jose, California, United States
Abstract
Texture segmentation and analysis is an important aspect of pattern recognition and digital image processing. Previous approaches to texture analysis and segmentation perform multi-channel filtering by applying a set of filters to the image. In this paper we describe a texture segmentation algorithm based on multi-channel filtering that is optimized using diagonal high frequency residual. Gabor band pass filters with different radial spatial frequencies and different orientations have optimum resolution in time and frequency domain. The image is decomposed by a set of Gabor filters into a number of filtered images; each one contains variation of intensity on a sub-band frequency and orientation. The features extracted by Gabor filters have been applied for image segmentation and analysis. There are some important considerations about filter parameters and filter bank coverage in frequency domain. This filter bank does not completely cover the corners of the frequency domain along the diagonals. In our method we optimize the spatial implementation for the Gabor filter bank considering the diagonal high frequency residual. Segmentation is accomplished by a feedforward backpropagation multi-layer perceptron that is trained by optimized extracted features. After MLP is trained the input image is segmented and each pixel is assigned to the proper class.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nezamoddin Nezamoddini-Kachouie and Javad Alirezaie "Optimized multichannel decomposition for texture segmentation using Gabor filter bank", Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); https://doi.org/10.1117/12.526300
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KEYWORDS
Image segmentation

Image filtering

Linear filtering

Gaussian filters

Feature extraction

Spatial resolution

Analytical research

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