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Yoav Rosenberg

dblp:60/2807 · 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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorArtificial 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.

Theoretical computer science
1 paper
Information theory · 100%
Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%

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

TopicWeightPapersLastEvidence papers
Information theory › signal processing › filtering
nonlinear filtering		 0.011997
A general filter for measurements with any probability distribution · CVPR 1997
Information theory › estimation theory
state estimation		 0.011997
A general filter for measurements with any probability distribution · CVPR 1997
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian filtering		 0.011997
A general filter for measurements with any probability distribution · CVPR 1997
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian filtering
kalman filtering		 0.011997
A general filter for measurements with any probability distribution · CVPR 1997

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

probability distribution approximation · 0.0
YearPublicationVenuePosition
1998 Real-time object tracking from a moving video camera: a software approach on a PC
abstract
We demonstrate a real time system for image registration and moving object detection. The algorithm is based on describing the displacement of a point as a probability distribution over a matrix of possible displacements. A small set of randomly selected points is used to compute the registration parameters. Moving object detection is based on the consistency of the probabilistic displacement of image points with the global image motion.
Yoav Rosenberg, Michael Werman
 WACV1
1997 A general filter for measurements with any probability distribution
abstract
The Kalman filter is a very efficient optimal filter, however it has the precondition that the noises of the process and of the measurement are Gaussian. The authors introduce 'the general distribution filter' which is an optimal filter that can be used even where the distributions are not Gaussian. An efficient practical implementation of the filter is possible where the distributions are discrete and compact or can be approximated as such.
Yoav Rosenberg, Michael Werman
 CVPR1