W
Wageeh Boles
Researcher at Queensland University of Technology
Publications - 162
Citations - 3781
Wageeh Boles is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Wavelet transform & Wavelet. The author has an hindex of 29, co-authored 162 publications receiving 3589 citations. Previous affiliations of Wageeh Boles include Commonwealth Scientific and Industrial Research Organisation & University of Pittsburgh.
Papers
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Journal ArticleDOI
A human identification technique using images of the iris and wavelet transform
Wageeh Boles,Boualem Boashash +1 more
TL;DR: A new approach for recognizing the iris of the human eye is presented, and the resulting one-dimensional signals are compared with model features using different dissimilarity functions.
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Recognition of 2D object contours using the wavelet transform zero-crossing representation
Quang M. Tieng,Wageeh Boles +1 more
TL;DR: A new algorithm to recognize a two-dimensional object of arbitrary shape is presented and shows that, compared with the use of Fourier descriptors, this algorithm gives more stable and accurate results.
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Texture for script identification
TL;DR: In this article, the authors investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture, and provide a qualitative measure of which texture features are most appropriate for this task.
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A Review of Medical Image Watermarking Requirements for Teleradiology
TL;DR: The security requirements of medical images as well as expected threats in teleradiology are reviewed and justification for watermarking over conventional security measures is made in terms of their various objectives, properties, and requirements.
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Wavelet-based affine invariant representation: a tool for recognizing planar objects in 3D space
Quang M. Tieng,Wageeh Boles +1 more
TL;DR: Experimental results show that the performance of the proposed representation of planar objects undergoing a general affine transformation is better than that of other existing methods, particularly when objects are heavily corrupted with noise.