Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Bernard Sarel

dblp:56/1034 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
0since 2021 · last 2005
—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 · 2 · 2 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 · 75% Audio and music processing · 25%
Artificial intelligence
1 paper
Video understanding and tracking · 100%

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

TopicWeightPapersLastEvidence papers
Image and video processing › image decomposition › image separation › layer separation
transparent layer separation
0.122005
Separating Transparent Layers of Repetitive Dynamic Behaviors · ICCV 2005
Separating Transparent Layers through Layer Information Exchange · ECCV (4) 2004
Computer vision › Video understanding and tracking
motion analysis
0.112005
Separating Transparent Layers of Repetitive Dynamic Behaviors · ICCV 2005
Audio and music processing
source separation
0.112005
Separating Transparent Layers of Repetitive Dynamic Behaviors · ICCV 2005
Image and video processing › image decomposition › image separation
layer separation
0.012004
Separating Transparent Layers through Layer Information Exchange · ECCV (4) 2004

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

spatio-temporal alignment · 0.1median filtering · 0.1
YearPublicationVenuePosition
2005 Separating Transparent Layers of Repetitive Dynamic Behaviors
abstract
In this paper, we present an approach for separating two transparent layers of complex nonrigid scene dynamics. The dynamics in one of the layers is assumed to be repetitive, while the other can have any arbitrary dynamics. Such repetitive dynamics includes, among other, human actions in video (e.g., a walking person), or a repetitive musical tune in audio signals. We use a global to local space time alignment approach to detect and align the repetitive behavior. Once aligned, a median operator applied to space time derivatives is used to recover the intrinsic repeating behavior, and separate it from the other transparent layer. We show results on synthetic and real video sequences. In addition, we show the applicability of our approach to separating mixed audio signals (from a single source).
Bernard Sarel, Michal Irani
ICCV1
2004 Separating Transparent Layers through Layer Information Exchange
Bernard Sarel, Michal Irani
ECCV (4)1