Computer Vision, Graphics, and Image Processing
Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
SIBGRAPHI '98 Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision
Texture Analysis Using Morphological Pattern Spectrum and Optimization of Structuring Elements
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Texture Modeling by Optimal Gray Scale Structuring Elements Using Morphological Pattern Spectrum
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
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In this paper, we address one very important industrial application of computer vision – automatic classification of materials. In our work, we have considered materials that are mixtures of two or more elements. Such materials are called alloys. It is observed at the microscopic level that an alloy is composed of small randomly distributed crystals of varying shapes and sizes called grains. Also, the color and hence the intensity of the grains vary in alloys. Generally, this shape-size-intensity distribution of the grains is different for different materials. This means micrographs obtained from different materials form texture-like images that differ from one material to another in appearance. Therefore, in principle, any texture analysis method may be used for material classification. In our method, we propose to extract textural features corresponding to grain geometry and intensity and use them for analysis and classification of alloys. These features are extracted via gray-scale morphological operations and are measured in terms of Size-Intensity-Diagram (SID) and Tri-variate Pattern Spectrum (TPS) coefficients. In our experiments, we achieved 83.43% and 89.43% classification accuracies in cases of SID and TPS, respectively. This demonstrates the effectiveness of the proposed method for material classification which in turn confirms that our choice of features is indeed appropriate for the purpose.