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العنوان
Fabric Defect Detection Based on Image Processing Algorithms /
المؤلف
Zahra, Aya Gamal Zaki
هيئة الاعداد
باحث / أية جمال ذكي زهرة
مشرف / محمد أمين
مشرف / فتحي السيد عبد السميع
مشرف / محمود امام عبد المحسن امام
الموضوع
Histogram properties approach Pooling Layer
تاريخ النشر
2023
عدد الصفحات
100 p.:
اللغة
الإنجليزية
الدرجة
الدكتوراه
التخصص
Artificial Intelligence
تاريخ الإجازة
7/12/2023
مكان الإجازة
جامعة المنوفية - كلية العلوم - الرياضيات وعلوم الحاسب
الفهرس
Only 14 pages are availabe for public view

from 119

from 119

Abstract

We introduced a fabric defect segmentation method based on a Simplified Pulse
Coupled Neural Network (SPCNN). Firstly, the quality of the fabric images is improved
using contrast image enhancement techniques, including Histogram Equalization (HE),
fuzzy technique and super resolution (SR). Secondly, the enhanced fabric images are
segmented using SPCNN to detect the defective regions. Finally, the SPCNN
performance is tested on FI-1662 grayscale dataset and compared to Entropy Otsu
automatic threshold, Gray Level Co-occurrence Matrix (GLCM) and Watershed methods.
The proposed SPCNN method achieves segmentation accuracy up to 99.67 %, in finding
the defective regions.