Record Details

<p>Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface</p>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p>Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface</p>
 
Added Entry - Uncontrolled Name Mohammed, Khwaja Muinuddin Chisti; JNTUK, Andhra Pradesh, India
Kumar, S Srinivas; JNTUK, Andhra Pradesh, India
Prasad, Gandikota ; SR International Institute of Tech., RR District, Hyderabad, India
 
Uncontrolled Index Term Contrast Enhancement; Grey Wolf Optimization; Monarch Butterfly Optimization (MBO); Textile Industry
 
Summary, etc. <p style="text-align: justify;">In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) &amp; Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2020-11-09 17:14:07
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/41777
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 9 (20)
 
Language Note en