آشكارسازي عيوب بافتي پارچه با استفاده از شكل بهبوديافته الگوي باينري محلي
محورهای موضوعی : مهندسی برق و کامپیوتر
فرشاد تاجری‌پور
1
,
احسان‌اله کبیر
2
,
عباس شیخی
3
1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
3 - دانشگاه شیراز
کلید واژه: آشكارسازيالگوي باينري محليبافتبينایي ماشينپارچهعيوب بافتي,
چکیده مقاله :
يكي از روش‌هايي كه در عين سادگي مي‌تواند ويژگي‌هاي مناسبي براي طبقه‌بندي بافت تصوير با دقت بالا توليد كند، الگوي باينري محلي است. در اين مقاله روشي براي آشكارسازي عيوب بافتي پارچه با استفاده از اين ويژگي‌ها ارائه شده است. ابتدا در مرحله آموزش، عملگر الگوي باينري محلي روي كل تصوير پارچه سالم پيكسل به پيكسل اعمال مي‌شود و بردار ويژگي‌هاي مبنا به دست مي‌آيد. سپس اين تصوير به پنجره‌هايي تقسيم شده و عملگر الگوي باينري محلي روي هر كدام از اين پنجره‌ها اعمال شده و بر اساس مقايسه با بردار ويژگي مبنا يك حد آستانه مناسب براي سالم‌بودن پنجره‌ها محاسبه مي‌شود. در هنگام آشكارسازي، تصوير مورد بررسي به پنجره‌هايي تقسيم شده و با استفاده از حد آستانه محاسبه‌شده، پنجره‌هايي كه به قسمت‌هاي معيوب تصوير تعلق دارند مشخص مي‌شود. روش ارائه‌شده نسبت به انتقال تصوير و تغيير شدت روشنايي نقاط تصوير حساس نيست و از آن مي‌توان براي آشكارسازي عيوب بافتي در پارچه‌هاي بدون طرح و پارچه‌هاي طرح‌دار استفاده كرد. با توجه به سادگي روش، پياده‌سازي آن به صورت برخط ميسر است. نتايج به دست آمده نشان مي‌دهد كه دسته وسيعي از عيوب بافتي پارچه با اين روش به صورت مطلوب قابل آشكارسازي هستند.
One of the methods which can produce powerful features for texture classification is Local Binary Patterns, LBP. In this paper we propose a method for defect detection in textile fabrics using these features. In the training stage, at first step LBP operator is applied to an image of defect free fabric, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied on each of these windows. Based on comparison to the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is gray scale and shift invariant and can be used for defect detection in patterned and plain fabrics. Due to its simplicity online implementation is possible.
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