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Peter C. Gaston

dblp:46/9411 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1984
—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 · 60% Robot manipulation · 40%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › object recognition
model-based object recognition
0.011984
Tactile Recognition and Localization Using Object Models: The Case of Polyhedra on a Plane · IEEE Trans. Pattern Anal. Mach. Intell. 1984
Computer vision › Image recognition and object detection
object localization
0.011984
Tactile Recognition and Localization Using Object Models: The Case of Polyhedra on a Plane · IEEE Trans. Pattern Anal. Mach. Intell. 1984
Computer vision › Image recognition and object detection
object recognition
0.011984
Tactile Recognition and Localization Using Object Models: The Case of Polyhedra on a Plane · IEEE Trans. Pattern Anal. Mach. Intell. 1984
Robotics › Robot manipulation › tactile sensing
tactile localization
0.011984
Tactile Recognition and Localization Using Object Models: The Case of Polyhedra on a Plane · IEEE Trans. Pattern Anal. Mach. Intell. 1984
Robotics › Robot manipulation › tactile sensing
tactile object recognition
0.011984
Tactile Recognition and Localization Using Object Models: The Case of Polyhedra on a Plane · IEEE Trans. Pattern Anal. Mach. Intell. 1984

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

hypothesis tree pruning · 0.0
YearPublicationVenuePosition
1984 Tactile Recognition and Localization Using Object Models: The Case of Polyhedra on a Plane
abstract
This paper discusses how data from multiple tactile sensors may be used to identify and locate one object, from among a set of known objects. We use only local information from sensors: 1) the position of contact points and 2) ranges of surface normals at the contact points. The recognition and localization process is structured as the development and pruning of a tree of consistent hypotheses about pairings between contact points and object surfaces. In this paper, we deal with polyhedral objects constrained to lie on a known plane, i.e., having three degrees of positioning freedom relative to the sensors. We illustrate the performance of the algorithm by simulation.
Peter C. Gaston, Tomás Lozano-Pérez
IEEE Trans. Pattern Anal. Mach. Intell.1