Quality control is an essential step in the textile manufacturing industry. There is a growing interest in the field of automation using computer vision for freeing human beings from the inspection task. In this paper, patterned fabric images are analyzed using entropy cues in order to detect different kinds of defects. In our proposal, we transform the test image to an entropy image in which the defects show low values and can be easily separated by a simple thresholding. Our method is evaluated and compared with previously proposed approaches, showing better results on an extensive database of real defective and non-defective fabrics.
CITATION STYLE
Martinez-Leon, M., Lizarraga-Morales, R. A., Rodriguez-Donate, C., Cabal-Yepez, E., & Mata-Chavez, R. I. (2016). Defect detection on patterned fabrics using entropy cues. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9680, pp. 71–78). Springer Verlag. https://doi.org/10.1007/978-3-319-33618-3_8
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