Surface Defect Identification and Grouping of Intermittent Leather Images using Linear Discriminant Model
S. NithiyananthaVasagam1, M. Sornam2
1S. Nithiyanantha Vasagam*, Master of Computer Science, Madurai Kamaraj University, Madurai, Tamil Nadu, India.
2Dr M. Sornam, Professor, Department of Computer Science, University of Madras, Tamil Nadu, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2253-2259 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25271081219/2019©BEIESP | DOI: 10.35940/ijitee.L2527.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Scrutiny of intermittent leather is accepted through visual analysis on the natural material by an experienced individual based on many parameters which includes surface defects as a parameter. Such results comprising of base color, other than base color, share of regions, share of cutting area, share of cutting value, position wise length and position wise breadth will determine the value of the leather and surprisingly the result will vary form one experienced person to another. Hence, a new method for grouping of intermittent leather is proposed for a better or suitable decision making. Feature extraction technique, Grey Level Co-occurrence Matrix (GLCM) has been implemented to understand the features of color and area by extracting the texture features like Entropy, Energy, Contrast, Variance, Mean, Dissimilarity, Correlation and Homogeneity. A total of 428 intermittent leather imagesare used to understand the classification. The classifiers, Linear Discriminant Analysis (LDA) model and Support Vector Machine (SVM) are used to find out the accuracy. Further, linear discriminant model confirms 92% of accuracy over the support vector machine which is confirms 89.65% of accuracy. The proposed LDA model clearly shows that the approach is successful in classifying the variations among the defects and non-defects in intermittent leather images.
Keywords: Grey Level Co-occurrence Matrix, Intermittent leather, Linear Discriminant Analysis and Support Vector Machine
Scope of the Article: Image Processing