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dc.contributor.advisor Goneid, Amr Habib, Mary 2013-05-23T08:47:07Z 2013-05-23T16:00:04Z 2003 Spring en 2013-05-23
dc.description.abstract The work presented in this thesis deals with the application of spectral methods for texture classification. The aim of the present work is to introduce a hybrid methodology for texture classification based on a spatial domain global pre-classifier together with a spectral classifier that utilizes multiresolution transform analysis. The reason for developing a spatial pre-classifier is that many discriminating features of textures are present in the spatial domain of the texture. Of these, global features such as intensity histograms and entropies can still add significant information to the texture classification process. The pre-classifier uses texture intensity histograms to derive histogram moments that serve as global features. A spectral classifier that uses Hartley transform follows the pre-classifier. The choice of such transform was due to the fact that the Fast Hartley Transform has many advantages over the other transforms since it results in real valued arrays and requires less memory space and computational complexity. To test the performance of the whole classifier, 900 texture images were generated using mathematical texture generating functions. The images generated were of three different classes and each class is sub-classified into three sub-classes. Half of the generated samples was used to build the classifier, while the other half was used to test it. The pre-classifier was designed to identify texture classes using an Euclidean distance matching for 4 statistical moments of the intensity histograms. The pre-classifier matching accuracy is found to be 99.89%. The spectral classifier is designed on the basis of the Hartley transform to determine the image sub-class. Initially, a full resolution Hartley transform was used to obtain two orthogonal power spectral vectors. Peaks in these two vectors were detected after applying a 10% threshold and the highest 4 peaks for each image are selected and saved in position lookup tables. The matching accuracy obtained using the two classification phases (pre-classifier and spectral classifier) is 99.56%. The accuracy achieved for the single resolution classifier is high but that was achieved on the expense of space for the lookup tables. In order to investigate the effect of lowering the resolution on the size of the information needed for matching the textures, we have applied a multiresolution technique to the Hartley Transform in a restricted way by computing the Hartley spectra in decreasing resolution. In particular, a one-step resolution decrease achieves 99% matching efficiency while saving memory space by 40%. This is a minor sacrifice of less than 1% in the matching efficiency with a considerable decrease in the complexity of the present methodology. en
dc.format.extent 103 p. en
dc.format.medium theses en
dc.language.iso en en
dc.rights Author retains all rights with regard to copyright. en
dc.subject Materials en
dc.subject Texture en
dc.subject Data processing. en
dc.subject.lcsh Thesis (M.A.)--American University in Cairo en
dc.title Texture classification using transform analysis en
dc.type Text en
dc.subject.discipline Computer Science en
dc.rights.access This item is available en
dc.contributor.department American University in Cairo. Dept. of Computer Science and Engineering en
dc.description.irb American University in Cairo Institutional Review Board approval is not necessary for this item, since the research is not concerned with living human beings or bodily tissue samples. en
dc.contributor.committeeMember Goneid, Amr

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  • Theses and Dissertations [1704]
    This collection includes theses and dissertations authored by American University in Cairo graduate students.

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