This paper presents and evaluates a pixel-based texture classifier that integrates multiple texture feature extraction methods through a new scheme based on the Kullback J-divergence. Experimental results show that the proposed technique yields qualitatively better image segmentations than well-known both supervised and unsupervised texture classifiers based on specific families of texture methods. A practical application to fabric defect detection is presented. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Garcia, M. A., & Puig, D. (2003). Pixel classification by divergence-based integration of multiple texture methods and its application to fabric defect detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2781, 132–139. https://doi.org/10.1007/978-3-540-45243-0_18
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