Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
A Selective Attention Computational Model for Perceiving Textures
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
A biologically-inspired computational model for perceiving the TROIs from texture images
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Unsupervised approach for extracting the textural region of interest from real image
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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Texture analysis is an important technique in many image processing areas, such as scene segmentation, object recognition, and shape&depth perception. However, most methods are restricted to issue of computational complexity and supervised problems. Accordingly, we propose a efficient method of segmenting texture that uses unsupervised learning schemes to discover a texture cluster without a pre-knowledge. This method applies 2D Gaussian filters to the clustered region iteratively, and the thresholding value for segmenting is automatically determined by analyzing histogram of the clustered inner-region. It can be acquired by the boundary tracing in the clustered region. In order to show the performance of the proposed method, we have attempted to build a various texture images, and the segmenting quality was measured according to the goodness based on the segmented shape of region. Our experimental results showed that the performance of the proposed method is very successful.