Title: Detection of fabric defects using fuzzy decision tree
Authors: Madasu Hanmandlu; Dilip K. Choudhury; Sujata Dash
Addresses: Department of Electrical Engineering, IIT, Delhi, India ' GIET - Gandhi Institute of Engineering and Technology, Gunupur, Odissa, India ' GIFT - Gandhi Institute for Technology, Bhubaneswar, India
Abstract: This paper presents the representation of fabric texture by four features: Local Binary Patterns (LBP), Local Directional Patterns (LDP), Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF). The features extracted by these approaches are used in the Fuzzy Decision Tree (FDT) to detect defects in fabrics. We employ both fuzzy Gini index and fuzzy Shannon entropy as the splitting criteria. Two membership functions: Gaussian and trapezoidal are employed for the fuzzification of the genuine and imposter scores. A stopping criterion is devised to terminate the FDT. It is found that LDP features outperform LBP, SIFT and SURF features in the classification of defects in fabrics.
Keywords: LBP; local binary patterns; LDP; local directional patterns; SIFT; SURF; Gaussian; trapezoidal; fuzzy Gini index; fuzzy Shannon entropy; FDT; fuzzy decision trees; defect detection; fabric defects; fabric texture; feature extraction; fabrics.
International Journal of Signal and Imaging Systems Engineering, 2016 Vol.9 No.3, pp.184 - 198
Received: 21 May 2013
Accepted: 09 Apr 2014
Published online: 30 Apr 2016 *