dc.creatorScharcanski, Jacob
dc.date2011-01-29T06:00:38Z
dc.date2007
dc.identifier1083-4427
dc.identifierhttp://hdl.handle.net/10183/27605
dc.identifier000615536
dc.descriptionSeveral continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing important information about both the materials produced and the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among industrial stochastic images or to discriminate between texture images of stochastic materials with distinct properties. Nowadays, the degree of discrimination required by industrial processes often goes beyond the limits of human visual perception. This paper proposes to model this specific class of textures as colored noise and presents a new approach for multiresolution stochastic texture representation and discrimination in industry (e.g., nonwoven textiles and paper). The wavelet transform is used to represent stochastic texture images in multiple resolutions and to describe them using local orientation and density variability as features. Based on this representation, a multiresolution distance measure for stochastic textures is proposed, and industrial applications of the method and experimental results are reported. The conclusions include ideas for future work.
dc.formatapplication/pdf
dc.languageeng
dc.relationIEEE transactions on systems, man, and cybernetics. Part A : Systems and humans. Vol. 37, no. 1 (Jan. 2007), p. 10-22
dc.rightsOpen Access
dc.subjectAnisotropy
dc.subjectColored noise
dc.subjectIndustrial quality control
dc.subjectMaintenance
dc.subjectNonwoven textiles
dc.subjectStochastic textures
dc.subjectWavelets
dc.subjectAutomação industrial
dc.subjectReconhecimento : Padroes
dc.titleA wavelet-based approach for analyzing industrial stochastic textures with applications
dc.typeArtigo de periódico
dc.typeEstrangeiro


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