Sum and Difference Histograms for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Spatial Texture Analysis: A Comparative Study
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
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
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Texture Analysis
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A new approach for texture classification in CBIR
International Journal of Computer Applications in Technology
Local resampling for patch-based texture synthesis in vector fields
International Journal of Computer Applications in Technology
Using neural networks to monitor supply chain behaviour
International Journal of Computer Applications in Technology
Using support vector machine for characteristics prediction of hydraulic valve
International Journal of Computer Applications in Technology
Inspection of surface defects in copper strip using multivariate statistical approach and SVM
International Journal of Computer Applications in Technology
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Texture is an important attribute to distinguish objects and materials. Thus, along the decades many texture analysis methods have been proposed and utilised in a variety of application domains. Due to the fact there is not a generic method to describe a large variety of textures, comparative studies among the related methods became necessary. This paper describes a comparative study of the main statistical methods applied to materials surface characterisation. In order to evaluate the performance of the compared methods, an unsupervised neural network was used to classify a set of 3,000 textures images, divided in five categories, with different levels of details. Inferences from this work could assist those ones that intend to perform some tasks involving automatic inspection of texture, mainly in materials science context.