IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Ten lectures on wavelets
Handbook of pattern recognition & computer vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification Using Windowed Fourier Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using wavelet transform
Pattern Recognition Letters
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Plant leaf identification using Gabor wavelets
International Journal of Imaging Systems and Technology
Texture analysis and classification using deterministic tourist walk
Pattern Recognition
A simplified gravitational model for texture analysis
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Lacunarity as a texture measure for address block segmentation
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A simplified gravitational model to analyze texture roughness
Pattern Recognition
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
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Textures are among the most important features in the field of image analysis. This paper presents an innovative methodology to extract information from them, converting an image into a simplified dynamical system in gravitational collapse process whose collapsing states are described by using the lacunarity method. The paper compares the proposed approach to other classical methods using Brodatz textures and a second texture database as benchmark. The best classification results using the standard parameters of the method were 97.00 % and 54.10 % of success rate (percentage of samples correctly classified) for both databases, respectively. These results prove that the presented approach is an efficient tool for texture analysis.