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Jeffrey D. Kymer

dblp:157/2724 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

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

Software engineering, systems software and programming languages · 3Artificial intelligence and machine learning · 1

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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › feature detection
corner detection
0.212015
Laplacian Scale-Space Behavior of Planar Curve Corners · IEEE Trans. Pattern Anal. Mach. Intell. 2015
Image and video processing
feature detection
0.212015
Laplacian Scale-Space Behavior of Planar Curve Corners · IEEE Trans. Pattern Anal. Mach. Intell. 2015

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

laplacian of gaussian · 0.2curvature scale space · 0.2
YearPublicationVenuePosition
2016 A component recommender for bug reports using Discriminative Probability Latent Semantic Analysis
Meng Yan 0001, Xiaohong Zhang 0002, Dan Yang 0001, Jeffrey D. Kymer
Inf. Softw. Technol.5
2016 Automatically classifying software changes via discriminative topic model: Supporting multi-category and cross-project
Meng Yan 0001, Xiaohong Zhang 0002, Dan Yang 0001, Jeffrey D. Kymer
J. Syst. Softw.6
2015 Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation
Meng Yan 0001, Xiaohong Zhang 0002, Dan Yang 0001, Jeffrey D. Kymer
Inf. Softw. Technol.6
2015 Laplacian Scale-Space Behavior of Planar Curve Corners
abstract
Scale-space behavior of corners is important for developing an efficient corner detection algorithm. In this paper, we analyze the scale-space behavior with the Laplacian of Gaussian (LoG) operator on a planar curve which constructs Laplacian Scale Space (LSS). The analytical expression of a Laplacian Scale-Space map (LSS map) is obtained, demonstrating the Laplacian Scale-Space behavior of the planar curve corners, based on a newly defined unified corner model. With this formula, some Laplacian Scale-Space behavior is summarized. Although LSS demonstrates some similarities to Curvature Scale Space (CSS), there are still some differences. First, no new extreme points are generated in the LSS. Second, the behavior of different cases of a corner model is consistent and simple. This makes it easy to trace the corner in a scale space. At last, the behavior of LSS is verified in an experiment on a digital curve.
Xiaohong Zhang 0002, Ying Qu 0007, Dan Yang 0001, Hongxing Wang 0001, Jeffrey D. Kymer
IEEE Trans. Pattern Anal. Mach. Intell.5