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Frank J. Pipitone

dblp:07/1639 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 1993
—ORCID · none

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

Artificial intelligence and machine learning · 4 · 2 first-authorSystems, architecture and hardware · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 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
3 papers
Image recognition and object detection · 44% 3D vision · 44% Knowledge representation and reasoning · 12%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%

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
0.021993
Rapid recognition of freeform objects in noisy range images using tripod operators · CVPR 1993
Tripod operators for recognizing objects in range images: rapid rejection of library objects · ICRA 1992
Computer vision › 3D vision › 3d object recognition
range image object recognition
0.021993
Rapid recognition of freeform objects in noisy range images using tripod operators · CVPR 1993
Tripod operators for recognizing objects in range images: rapid rejection of library objects · ICRA 1992
Geometric modeling and processing
shape analysis
0.011992
Tripod operators for recognizing objects in range images: rapid rejection of library objects · ICRA 1992
Knowledge, reasoning and agents › Knowledge representation and reasoning
model-based reasoning
0.011983
Model-Based Probabilistic Reasoning for Electronics Troubleshooting · IJCAI 1983
Knowledge, reasoning and agents › Knowledge representation and reasoning
probabilistic reasoning
0.011983
Model-Based Probabilistic Reasoning for Electronics Troubleshooting · IJCAI 1983

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

tripod operator · 0.0nearest-neighbor classification · 0.0nearest neighbor classification · 0.0
YearPublicationVenuePosition
1993 Rapid recognition of freeform objects in noisy range images using tripod operators
abstract
The tripod operator is a class of feature extraction operators for range images. It facilitates the recognition and/or localization of objects by extracting a few sparse point samples in a regimented way; n sampled surface points yield only n-3 scalar features containing all the surface shape information in these points and no other information. They have complete six degrees-of-freedom (DOF) isometry invariance. Isolated objects are recognized in noisy range images using a statistical treatment of a few isolated tripod operator placements. Experiments show fast, reliable recognition.>
Frank J. Pipitone, William Adams
CVPR1
1992 Tripod operators for recognizing objects in range images: rapid rejection of library objects
abstract
The tripod operator, a class of feature extraction operators for range images which facilitate the recognition and localization of objects is described. It consists of three points in 3-space fixed at the vertices of an equilateral triangle and a procedure for making several scalar measurements in the coordinate frame of the triangle. The triangle is moved as a rigid body until the three vertices lie on the surface of some range image or modeled object. The resulting measurements are local shape features which are invariant under rigid motions. These features contain all the shape information in the surface points involved, and no other information. Tripod operators are applicable to all 3-D shapes. A cloud of points in feature space was generated for each object by random placement of the operator. Then new feature measurements were made by operator placements in a range image containing one of those objects. Using a simple nearest-neighbor approach, the authors determined which objects were rejected and which remained as recognition candidates. Experiments showed that tripod operators had excellent discriminating power.>
Frank J. Pipitone, William Adams
ICRA1
1991 Experiments with the subsumption architecture
abstract
The subsumption architecture (SA) is a special case of behavior-based control for robotics. Behavioral modules are added as 'layers', with each layer performing a complete behavior. Higher-level behaviors override lower level ones by taking control of their effectors or manipulating their internal states. The control layers are built of finite-state machines connected by links that act essentially like wires. A prototype airplane controller made with the architecture was developed. This controller flies a simulated aircraft from take-off to landing and was run on a C-based implementation of the subsumption architecture. It was noted that the SA as currently defined is not sufficiently modular, a clean interface between different behaviors would be desirable, and a more general relationship than strict hierarchy between high-level and low-level modules is required. None of these problems is insoluble within the behavior-based approach, but all must be solved if realistic problems are to be dealt with. Some candidate solutions are given.>
Ralph Hartley, Frank J. Pipitone
ICRA2
1983 Model-Based Probabilistic Reasoning for Electronics Troubleshooting
Richard R. Cantone, Frank J. Pipitone, W. Brent Lander, Michael P. Marrone
IJCAI2