This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).
By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.
This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.
Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.
This work was supported by the National Natural Science Foundation of China (Grants Nos 11572084, 11472061 and 71371046), the Fundamental Research Funds for the Central Universities and DHU Distinguished Young Professor Program, and Babar Khan was supported by a grant from China Scholarship Council (CSC).
Khan, B., Han, F., Wang, Z. and Masood, R. (2016), "Bio-inspired approach to invariant recognition and classification of fabric weave patterns and yarn color", Assembly Automation, Vol. 36 No. 2, pp. 152-158. https://doi.org/10.1108/AA-11-2015-100Download as .RIS
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