ICANNGA 2011: Adaptive and Natural Computing Algorithms pp 118-126 | Cite as
A Model of Saliency-Based Selective Attention for Machine Vision Inspection Application
Conference paper
Abstract
A machine vision inspection model of surface defects, inspired by the methodologies of neuroanatomy and psychology, is investigated. Firstly, the features extracted from defect images are combined into a saliency map. The bottom-up attention mechanism then obtains ‘‘what’’ and ‘‘where’’ information. Finally, the Markov model is used to classify the types of the defects. Experimental results demonstrate the feasibility and effectiveness of the proposed model with 94.40% probability of accurately detecting of the existence of cropper strips defects.
Keywords
Vision inspection Surface defect Saliency map Selective attention Markov modelPreview
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