The 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. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1996.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/6094 |
| Date | January 1996 |
| Creators | Moodley, Deshendran. |
| Contributors | Ram, Vevek., Haines, Linda M. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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