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

L. Wixson

dblp:41/5007 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

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

Artificial 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.

Artificial intelligence
1 paper
Video understanding and tracking · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking
motion detection
0.012000
Detecting Salient Motion by Accumulating Directionally-Consistent Flow · IEEE Trans. Pattern Anal. Mach. Intell. 2000
Computer vision › Video understanding and tracking › motion detection
salient motion detection
0.012000
Detecting Salient Motion by Accumulating Directionally-Consistent Flow · IEEE Trans. Pattern Anal. Mach. Intell. 2000
Image and video processing › motion estimation
optical flow
0.012000
Detecting Salient Motion by Accumulating Directionally-Consistent Flow · IEEE Trans. Pattern Anal. Mach. Intell. 2000

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

optical flow integration · 0.1directionally-consistent flow accumulation · 0.1
YearPublicationVenuePosition
2000 Detecting Salient Motion by Accumulating Directionally-Consistent Flow
abstract
Motion detection can play an important role in many vision tasks. Yet image motion can arise from "uninteresting" events as well as interesting ones. In this paper, salient motion is defined as motion that is likely to result from a typical surveillance target (e.g., a person or vehicle traveling with a sense of direction through a scene) as opposed to other distracting motions (e.g., the scintillation of specularities on water, the oscillation of vegetation in the wind). We propose an algorithm for detecting this salient motion that is based on intermediate-stage vision integration of optical flow. Empirical results are presented that illustrate the applicability of the proposed methods to real-world video. Unlike many motion detection schemes, no knowledge about expected object size or shape is necessary for rejecting the distracting motion.
L. Wixson
IEEE Trans. Pattern Anal. Mach. Intell.1