On the Imaging of Fractal Surfaces
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
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
On the optimality of Naïve Bayes with dependent binary features
Pattern Recognition Letters
2D Euclidean distance transform algorithms: A comparative survey
ACM Computing Surveys (CSUR)
Fractal dimension applied to plant identification
Information Sciences: an International Journal
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Plant Leaf Identification Using Multi-scale Fractal Dimension
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Scale Analysis of Several Filter Banks for Color Texture Classification
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Enhancing Gabor wavelets using volumetric fractal dimension
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
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Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.