پديد آورنده :
حيدري، مهدي
عنوان :
تعيين دانه بندي گندله هاي توليدي در مجتمع فولاد مباركه با استفاده از تكنيك هاي پردازش ديجيتال تصاوير و الگوريتم هاي هوشمند
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
مخابرات﴿سيستم﴾
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر
صفحه شمار :
ده،83ص.: مصور،جدول،نمودار
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
رسول امير فتاحي، بهزاد نظري
توصيفگر ها :
ماشين بردار پشتيبان , فضاي رنگ , ريخت شناسي , RTSP , POE
تاريخ نمايه سازي :
9/7/92
استاد داور :
شادرخ سماوي، محمدرضا احمدزاده
دانشكده :
مهندسي برق و كامپيوتر
چكيده فارسي :
به فارسي و انگليسي:قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
83 Iron Ore Green Pellet Diameter Measurement by Using of Image Processing Techniques Mahdi Heydari m heidari@ec iut ac ir Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Persian Supervisors Dr Rasool Amirfattahi fattahi@cc iut ac ir Dr Behzad Nazari nazari@cc iut ac ir Abstract Automatic Quality control is a vital process in many manufacturing process such as steel industry Pellet size monitoring and control is a critical process which is done in steel making to improve quality of products According to the technical reports pellet size should be fall in the range of 9 16 mm in diameter Larger or smaller pellets could degrade the final products and impose extra overheads to industry In this paper a new method is proposed for measuring the pellet size using practical Image Processing algorithms In this method active contour with Chen Vese method is used to eliminate the images backgrounds and achieving a distinguishable plot of the objects After detecting distinct elements existing in the image the number of pellets in each object is determined and each object is classified as singular double triple or more pellets using an SVM classifier Finally morphological methods are used to estimate the real size of pellets and the pellets size histogram is presented This practical method was applied in Mobarakeh Steel Complex where the method was tested on about 1000 prototypes Results showed that we have 95 1 of accuracy for detection of one pellet elements and in classification by SVM 95 6 of elements were classified correctly Kaywords SVM Chan Vese Pellet Morphology PDF created with pdfFactory trial version www pdffactory com
استاد راهنما :
رسول امير فتاحي، بهزاد نظري
استاد داور :
شادرخ سماوي، محمدرضا احمدزاده
