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

Nick Bennett

dblp:90/5356 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1999
—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.

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
Image and video processing › pattern detection › shape detection
ellipse detection
0.011999
A Method to Detect and Characterize Ellipses Using the Hough Transform · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Image and video processing › feature detection
hough transform
0.011999
A Method to Detect and Characterize Ellipses Using the Hough Transform · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Image and video processing › pattern detection
shape detection
0.011999
A Method to Detect and Characterize Ellipses Using the Hough Transform · IEEE Trans. Pattern Anal. Mach. Intell. 1999

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

projective geometry · 0.0
YearPublicationVenuePosition
1999 A Method to Detect and Characterize Ellipses Using the Hough Transform
abstract
We describe a new technique for detecting and characterizing ellipsoidal shapes automatically from any type of image. This technique is a single pass algorithm which can extract any group of ellipse parameters or characteristics which can be computed from those parameters without having to detect all five parameters for each ellipsoidal shape. Moreover, the method can explicitly incorporate any a priori knowledge the user may have concerning ellipse parameters. The method is based on techniques from projective geometry and on the Hough transform. This technique can significantly reduce interpretation and computation time by automatically extracting only those features or geometric parameters of interest from images and making exact use of a priori information.
Nick Bennett, Robert Burridge, Naoki Saito 0001
IEEE Trans. Pattern Anal. Mach. Intell.1