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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1422

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dc.contributor.authorGanotra, Dinesh-
dc.contributor.authorJoseph, Joby-
dc.contributor.authorSingh, Kehar-
dc.date.accessioned2006-02-25T08:42:05Z-
dc.date.available2006-02-25T08:42:05Z-
dc.date.issued2004-
dc.identifier.citationOptics and Lasers in Engineering, 42(2), 179-192en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/1422-
dc.description.abstractFeed-forward backpropagation neural network has been used in fringe projection profilometry for reconstruction of a three-dimensional (3D) object. A grating structure comprising two regions of different spatial periods is projected on the reference surface over which the object is placed. The shorter spatial period part of the grating is projected over the object, whereas the longer spatial period part is projected on the reference surface only. 3D object shape is reconstructed with the help of neural networks using images of the projected grating. During training phase of the network, the shorter spatial period grating along with the longer spatial period grating is used. Experimental results are presented for a diffuse object, showing that the 3D shape of the object is recovered using the above-mentioned method. However, the phases wrapping takes place in Fourier transform profilometry by using only one grating of shorter spatial period.en
dc.format.extent592722 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectNeural networksen
dc.subjectProfilometryen
dc.subject3D object shape reconstructionen
dc.titleObject reconstruction in multilayer neural network based profilometry using grating structure comprising two regions with different spatial periodsen
dc.typeArticleen
Appears in Collections:Physics

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