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) & 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.

中文翻译：

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皮革表面缺陷检测的优化模糊C-均值聚类方法

在本文中，将捕获的图像分割为有缺陷的部分，用于通过皮革，织物，纺织品，瓷砖等行业中使用的自动检查系统对产品质量进行进一步分级的过程。这些工业是最伟大的传统工业，需要自动检测系统作为减少调查时间和提高生产率的基本组成部分。最初，在这项工作中，输入的图像是湿蓝色皮革，并经过对比度增强处理来改善图像特征的可见性。此对比度增强的图像用于利用模糊C均值算法（FCM）技术的分割过程。本文提出了两种不同的优化技术：灰狼优化（GWO）和 在FCM中执行质心优化的帝王蝶优化（MBO），并将结果与​​皮革分割方法的GWO修正区域生长进行了比较。结果表明，与上下文技术相比，将优化技术与FCM结合对分割精度的影响非常明显，达到96.90％。