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John W. Gorman

dblp:09/6512 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1988
—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
Image recognition and object detection · 100%
Theoretical computer science
1 paper
Algorithms and data structures · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › shape recognition
partial shape recognition
0.011988
Partial Shape Recognition Using Dynamic Programming · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Computer vision › Image recognition and object detection
shape recognition
0.011988
Partial Shape Recognition Using Dynamic Programming · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Algorithms and data structures
dynamic programming
0.011988
Partial Shape Recognition Using Dynamic Programming · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Algorithms and data structures › sequence algorithms › string algorithms
sequence comparison
0.011988
Partial Shape Recognition Using Dynamic Programming · IEEE Trans. Pattern Anal. Mach. Intell. 1988

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

dynamic programming · 0.0fourier descriptors · 0.0fourier descriptor · 0.0
YearPublicationVenuePosition
1988 Partial Shape Recognition Using Dynamic Programming
abstract
A partial-shape-recognition technique utilizing local features described by Fourier descriptors is introduced. A dynamic programming formulation for shape matching is developed, and a method for comparison of match quality is discussed. This technique is shown to recognize unknown contours that may be occluded or that may overlap other objects. Precise scale information is not required, and the unknown objects may appear at any orientation with respect to the camera. The segment-matching dynamic programming method is contrasted with other sequence-comparison techniques that utilize dynamic programming. Experimental results are discussed that indicate that partial contours can be recognized with reasonable accuracy.>
John W. Gorman, Owen Robert Mitchell, Frank P. Kuhl
IEEE Trans. Pattern Anal. Mach. Intell.1