Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Texturing and modeling: a procedural approach
Texturing and modeling: a procedural approach
Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Gabor wavelets for statistical pattern recognition
The handbook of brain theory and neural networks
Texture classification using multiresolution Markov random field models
Pattern Recognition Letters
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Plant Leaf Identification Using Multi-scale Fractal Dimension
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Leaves shape classification using curvature and fractal dimension
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Color texture analysis based on fractal descriptors
Pattern Recognition
Scale and orientation matching for texture analysis and recognition
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Texture descriptor based on partially self-avoiding deterministic walker on networks
Expert Systems with Applications: An International Journal
Salient features selection for multiclass texture classification
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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One of the most important visual attributes for image analysis and pattern recognition is the texture. Its analysis allows to describe and identify different regions in the image through pixel organization, performing a better image description and classification. This paper presents a novel approach for texture analysis, based on calculation of the fractal dimension of binary images generated from a texture, using different threshold values. The proposed approach performs a complexity analysis as the threshold values changes, producing a texture signature which is able to characterize efficiently different texture classes. The paper illustrates the novel method performance on an experiment using Brodatz images.