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Mark W. Orletsky

dblp:81/3567 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none

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

Theory of computation · 2Artificial 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.

Theoretical computer science
3 papers
Computational geometry · 73% Algorithms and data structures · 17% Approximation and online algorithms · 10%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational geometry › geometric matching
geometric pattern matching		 0.011999
Approximate Geometric Pattern Matching Under Rigid Motions · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Computational geometry
point location		 0.011997
Methods for Achieving Fast Query Times in Point Location Data Structures · SODA 1997
Algorithms and data structures › sequence algorithms › string algorithms
string matching		 0.011994
Practical Methods for Approximate Geometric Pattern Matching Under Rigid Motions (Preliminary Version) · SCG 1994
Approximation and online algorithms
approximation algorithms		 0.011999
Approximate Geometric Pattern Matching Under Rigid Motions · IEEE Trans. Pattern Anal. Mach. Intell. 1999
Computational geometry
geometric data structures		 0.011997
Methods for Achieving Fast Query Times in Point Location Data Structures · SODA 1997

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

worst-case performance bounds · 0.0data structure design · 0.0branch-and-bound · 0.0
YearPublicationVenuePosition
1999 Approximate Geometric Pattern Matching Under Rigid Motions
abstract
We present techniques for matching point-sets in two and three dimensions under rigid-body transformations. We prove bounds on the worst-case performance of these algorithms to be within a small constant factor of optimal and conduct experiments to show that the average performance of these matching algorithms is often better than that predicted by the worst-case bounds.
Michael T. Goodrich, Joseph S. B. Mitchell, Mark W. Orletsky
 IEEE Trans. Pattern Anal. Mach. Intell.3
1997 Methods for Achieving Fast Query Times in Point Location Data Structures
Michael T. Goodrich, Mark W. Orletsky, Kumar Ramaiyer
 SODA2
1994 Practical Methods for Approximate Geometric Pattern Matching Under Rigid Motions (Preliminary Version)
abstract
We present practical methods for approximate geometric pattern matching in d-dimensions along with experimental data regarding the quality of matches and running times of these methods versus those of a branch-and-bound search. Our methods are faster than previous methods but still produce good matches.
Michael T. Goodrich, Joseph S. B. Mitchell, Mark W. Orletsky
 SCG3