Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of multiple Gabor filters for texture segmentation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
Development of a climbing robot for grit blasting operations in shipyards
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multi-channel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed.