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Ayman E. Ibrahim

dblp:11/475 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1996
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 67% Image and video coding · 33%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image transform
gabor transform		 0.011996
A fast learning algorithm for Gabor transformation · IEEE Trans. Image Process. 1996
Image and video processing
image representation		 0.011996
A fast learning algorithm for Gabor transformation · IEEE Trans. Image Process. 1996

Methods — techniques the papers use, named apart from their topics

recursive least squares · 0.0least mean squares · 0.0gabor transform · 0.0
YearPublicationVenuePosition
1996 A fast learning algorithm for Gabor transformation
abstract
An adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor (1946) transform representation is introduced. The algorithm uses a recursive least squares (RLS) type algorithm. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. The proposed RLS learning offers better accuracy and faster convergence behavior when compared with the least mean squares (LMS)-based algorithms. Applications of this scheme in image data reduction are also demonstrated.
Ayman E. Ibrahim, Mahmood R. Azimi-Sadjadi
 IEEE Trans. Image Process.1
1994 A Fast Learning Algorithm for Gabor Transform Extraction
abstract
A simple neural network-based approach is introduced in this paper which allows the computation of the coefficients of the generalized non-orthogonal 2-D Gabor transform representation. The network is trained using a recursive least squares (RLS) type algorithm. This RLS learning algorithm offers better accuracy and faster convergence when compared to the least mean squares (LMS) based algorithms. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. Application of this scheme in image data reduction is demonstrated in the simulation results.>
Ayman E. Ibrahim, Mahmood R. Azimi-Sadjadi, Sassan Sheedvash
 ISCAS1