Automated visual inspection for surgical instruments based on generalization of Bag-of-Words model

Murtadha Basil Abbas (P56104)

Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/476626

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DC Field	Value	Language
dc.contributor.advisor	Anton Satria Prabuwono, Assoc. Prof. Dr.
dc.contributor.author	Murtadha Basil Abbas (P56104)
dc.date.accessioned	2023-10-06T09:22:41Z	-
dc.date.available	2023-10-06T09:22:41Z	-
dc.date.issued	2012-08-30
dc.identifier.other	ukmvital:122172
dc.identifier.uri	https://ptsldigital.ukm.my/jspui/handle/123456789/476626	-
dc.description	Upon increasing the pressure of imposing high requirements in industrial sectors, the common and traditional methods of human subjective evaluations are gradually being replaced by the Machine Vision (MV) technique. In fact, this technique is called as Automated Visual Inspection System (AVIS). Along with the last few decades, the AVIS is playing an important role in either side of quality control and/or production process. At current state, AVIS applications are mostly developed based on shape and geometric feature extraction methods. Unfortunately, that approaches have some problems due to image scaling, illumination, noise and other variant issues. However, the main objective of this research is to introduce the spatial local feature to the AVIS development based on the generalization of Bag-of Words model (BoW) and to develop an intelligent image classification algorithm for the AVIS to classify the surgical instruments as a case study in order to measure the efficiency of the proposed method by comparing the classification result with the states-of-art model denoted by standard Scale Invariant Feature Transform (SIFT). Generally, the AVIS methodology covers two parts: the first one is the hardware installation, which consists of webcam, source light and the conveyor belt while the second one is the software techniques that comprising image processing and object recognition. Furthermore, this work is based on three criteria: image acquisition calibration, AVIS environmental conditions and (individual and group) classification technologies. Finally, the obtained results show that the proposed AVIS work can achieve up to 92% and 90% of accuracy rates for individual and group classifications in sequence for both of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) classification techniques.,Master of Information Technology,Certification of Master's / Doctoral Thesis" is not available"
dc.language.iso	eng
dc.publisher	UKM, Bangi
dc.relation	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat
dc.rights	UKM
dc.subject	Image processing -- Digital techniques -- Mathematics
dc.subject	Engineering inspection -- Automation
dc.subject	Computer vision
dc.subject	Universiti Kebangsaan Malaysia -- Dissertations
dc.subject	Dissertations, Academic -- Malaysia
dc.title	Automated visual inspection for surgical instruments based on generalization of Bag-of-Words model
dc.type	theses
dc.format.pages	111
dc.identifier.callno	TA1637.5.A234 2012 3 tesis
dc.identifier.barcode	005562(2021)(PL2)
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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