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Zarina Myles

dblp:43/2804 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1998
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 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 · 62% Multimedia analysis and retrieval · 38%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › image analysis › image blur analysis
defocus blur estimation
0.011998
Recovering Affine Motion and Defocus Blur Simultaneously · IEEE Trans. Pattern Anal. Mach. Intell. 1998
Image and video processing
image restoration
0.011998
Recovering Affine Motion and Defocus Blur Simultaneously · IEEE Trans. Pattern Anal. Mach. Intell. 1998
Computer vision › 3D vision
depth estimation
0.011996
Recovering Affine Motion and Defocus Blur Simultaneously · CVPR 1996
Computer vision › 3D vision
motion estimation
0.011996
Recovering Affine Motion and Defocus Blur Simultaneously · CVPR 1996
Image and video processing › motion estimation
affine motion estimation
0.011998
Recovering Affine Motion and Defocus Blur Simultaneously · IEEE Trans. Pattern Anal. Mach. Intell. 1998
Image and video processing
motion analysis
0.011998
Recovering Affine Motion and Defocus Blur Simultaneously · IEEE Trans. Pattern Anal. Mach. Intell. 1998

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

image formation modeling · 0.0defocus blur modeling · 0.0
YearPublicationVenuePosition
1998 Recovering Affine Motion and Defocus Blur Simultaneously
abstract
Motion in depth and/or zooming cause defocus blur. We show how the defocus blur in an image can be recovered simultaneously with affine motion. We introduce the theory, develop a solution method and demonstrate the validity of the theory and the solution by conducting experiments with real scenery.
Zarina Myles, Niels da Vitoria Lobo
IEEE Trans. Pattern Anal. Mach. Intell.1
1996 Recovering Affine Motion and Defocus Blur Simultaneously
abstract
There are at least two situations in practical computer vision where displacement of a point in an image is accompanied by a defocus blur. The first is when a camera of limited autofocal capability moves in depth, and the second is when a limited autofocal camera zooms. Motion and zooming are two popular strategies for acquiring more detail or for acquiring depth. The defocus blur has been considered noise or at best been ignored. However, the defocus blur is in itself a cue to depth, and hence we proceed to show how it can be calculated simultaneously with affine motion. We first introduce the theory, then develop a solution method and finally demonstrate the validity of the theory and the solution by conducting experiments with real scenery.
Zarina Myles, Niels da Vitoria Lobo
CVPR1