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H. James de St. Germain

dblp:139/4704 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 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
Geometric modeling and processing · 100%
Artificial intelligence
1 paper
3D vision · 100%

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

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
feature recognition
0.011999
Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999
Geometric modeling and processing › feature recognition
machining feature recognition
0.011999
Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999
Geometric modeling and processing
reverse engineering
0.011999
Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999
Computer vision › 3D vision
3d reconstruction
0.011999
Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999
Computer vision › 3D vision › 3d reconstruction › object reconstruction
CAD model reconstruction
0.011999
Feature-based reverse engineering of mechanical parts · IEEE Trans. Robotics Autom. 1999

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

feature-based modeling · 0.03d digitizer · 0.0
YearPublicationVenuePosition
1999 Feature-based reverse engineering of mechanical parts
abstract
Reverse engineering of mechanical parts requires extraction of information about an instance of a particular part sufficient to replicate the part using appropriate manufacturing techniques. This is important in a wide variety of situations, since functional CAD models are often unavailable or unusable for parts which must be duplicated or modified. Computer vision techniques applied to three-dimensional (3-D) data acquired using noncontact, 3-D position digitizers have the potential for significantly aiding the process. Serious challenges must be overcome, however, if sufficient accuracy is to be obtained and if models produced from sensed data are to be truly useful for manufacturing operations. The paper describes a prototype of a reverse engineering system which uses manufacturing features as geometric primitives. This approach has two advantages over current practice. The resulting models can be directly imported into feature-based CAD systems without loss of the semantics and topological information inherent in feature-based representations. In addition, the feature-based approach facilitates methods capable of producing highly accurate models, even when the original 3-D sensor data has substantial errors.
William B. Thompson, Jonathan C. Owen, H. James de St. Germain, Stevan R. Stark Jr., Thomas C. Henderson
IEEE Trans. Robotics Autom.3