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Gouda I. Salama

dblp:98/1670 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 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 coding · 50% Multimedia analysis and retrieval · 25% Image and video processing · 25%
Artificial intelligence
1 paper
Image recognition and object detection · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval
image analysis		 0.011998
Moment Invariants and Quantization Effects · CVPR 1998
Image and video coding › quantization
image quantization		 0.011998
Moment Invariants and Quantization Effects · CVPR 1998
Image and video processing › feature extraction
moment invariants		 0.011998
Moment Invariants and Quantization Effects · CVPR 1998
Image and video coding › quantization
quantization error analysis		 0.011998
Moment Invariants and Quantization Effects · CVPR 1998
Computer vision › Image recognition and object detection
shape recognition		 0.011998
Moment Invariants and Quantization Effects · CVPR 1998

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

hu moment invariants · 0.0error bound analysis · 0.0contour moments · 0.0
YearPublicationVenuePosition
2023 Regression layer-based convolution neural network for synthetic aperture radar images: de-noising and super-resolution
Aiman Mousa, Yasser Badran, Gouda I. Salama, Tarek A. Mahmoud
 Vis. Comput.3
1998 Moment Invariants and Quantization Effects
abstract
This paper considers the effect of spatial quantization on several moment invariants. Of particular interest are the affine moment invariants, which have emerged in recent years as a useful tool for image reconstruction, image registration, and recognition of deformed objects. Traditional analysis assumes moments and moment invariants for images that are defined in the continuous domain. In practice, however, the digitization process introduces errors that violate the invariance assumption. This paper presents an analysis of quantization-induced error on (two-dimensional) Hu moment invariants and affine moment invariants, and on invariants derived from (one-dimensional) contour moments. Error bounds are given in several cases.
Gouda I. Salama, A. Lynn Abbott
 CVPR1