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John L. Schneiter

dblp:64/6504 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1990
—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
Robot manipulation · 50% Image recognition and object detection · 50%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection
object recognition and localization
0.011990
An Automated Tactile Sensing Strategy for Planar Object Recognition and Localization · IEEE Trans. Pattern Anal. Mach. Intell. 1990
Robotics › Robot manipulation
tactile sensing
0.011990
An Automated Tactile Sensing Strategy for Planar Object Recognition and Localization · IEEE Trans. Pattern Anal. Mach. Intell. 1990

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

point contact sensing · 0.0geometric planning · 0.0
YearPublicationVenuePosition
1990 An Automated Tactile Sensing Strategy for Planar Object Recognition and Localization
abstract
The planning problem associated with tactile exploration for object recognition and localization is addressed. Given that an object has been sensed and is one of a number of modeled objects, and given that the data obtained so far are insufficient for recognition and/or localization, the methods developed determin the paths along which a point contact sensor must be directed in order to obtain further highly diagnostic measurements. Three families of sensor paths are found. The first is the family of paths for which recognition and localization are guaranteed. The second guarantees only that something will be learned. The third represents paths to avoid because nothing new will be learned. The methods are based on a small but powerful set of geometric ideas and are developed for two-dimensional, planar-faced objects. They are conceptually easily generalized to handle three-dimensional objects, including objects with through holes.>
John L. Schneiter, Thomas B. Sheridan
IEEE Trans. Pattern Anal. Mach. Intell.1