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dc.contributor.advisorRam, Vevek.
dc.contributor.advisorHaines, Linda M.
dc.creatorMoodley, Deshendran.
dc.date.accessioned2012-07-24T12:33:28Z
dc.date.available2012-07-24T12:33:28Z
dc.date.created1996
dc.date.issued1996
dc.identifier.urihttp://hdl.handle.net/10413/6094
dc.descriptionThesis (M.Sc.)-University of Natal, Pietermaritzburg, 1996.en
dc.description.abstractThe use of computers for digital image recognition has become quite widespread. Applications include face recognition, handwriting interpretation and fmgerprint analysis. A feature vector whose dimension is much lower than the original image data is used to represent the image. This removes redundancy from the data and drastically cuts the computational cost of the classification stage. The most important criterion for the extracted features is that they must retain as much of the discriminatory information present in the original data. Feature extraction methods which have been used with neural networks are moment invariants, Zernike moments, Fourier descriptors, Gabor filters and wavelets. These together with the Neocognitron which incorporates feature extraction within a neural network architecture are described and two methods, Zernike moments and the Neocognitron are chosen to illustrate the role of feature extraction in image recognition.en
dc.language.isoenen
dc.subjectOptical pattern recognition.en
dc.subjectImage processing--Digital techniques.en
dc.subjectNeural networks (Computer Science).en
dc.subjectComputer vision.en
dc.subjectArtificial intelligence.en
dc.subjectTheses--Computer science.en
dc.titleArtificial neural networks for image recognition : a study of feature extraction methods and an implementation for handwritten character recognition.en
dc.typeThesisen


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