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Tomer Amiaz

dblp:41/3716 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2009
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 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
2 papers
Image and video processing · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › texture analysis
dynamic texture analysis
0.112009
Dynamic Texture Detection Based on Motion Analysis · Int. J. Comput. Vis. 2009
Image and video processing › motion estimation
optical flow
0.112006
Piecewise-Smooth Dense Optical Flow via Level Sets · Int. J. Comput. Vis. 2006
Image and video processing
motion analysis
0.012009
Dynamic Texture Detection Based on Motion Analysis · Int. J. Comput. Vis. 2009
Image and video processing › mathematical imaging › partial differential equations for image processing
level set methods
0.012006
Piecewise-Smooth Dense Optical Flow via Level Sets · Int. J. Comput. Vis. 2006

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

spatiotemporal filtering · 0.1optical flow · 0.1level set · 0.1
YearPublicationVenuePosition
2009 Dynamic Texture Detection Based on Motion Analysis
Sándor Fazekas, Tomer Amiaz, Dmitry Chetverikov, Nahum Kiryati
Int. J. Comput. Vis.2
2007 Coarse to over-fine optical flow estimation
Tomer Amiaz, Eyal Lubetzky, Nahum Kiryati
Pattern Recognit.1
2006 Piecewise-Smooth Dense Optical Flow via Level Sets
Tomer Amiaz, Nahum Kiryati
Int. J. Comput. Vis.1
2005 Dense discontinuous optical flow via contour-based segmentation
abstract
We propose a new algorithm for dense optical flow computation. Dense optical flow schemes are challenged by the presence of motion discontinuities. In state of the art optical flow methods, over-smoothing of flow discontinuities accounts for most of the error. A breakthrough in the performance of optical flow computation has recently been achieved by Brox et al. Our algorithm embeds their functional within a contour-based segmentation framework. Piecewise-smooth flow fields are accommodated and flow boundaries are crisp. Experimental results show the superiority of our algorithm with respect to alternative techniques.
Tomer Amiaz, Nahum Kiryati
ICIP (3)1