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Sandra L. Bartlett

dblp:05/2 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 1991
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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.

Artificial intelligence
3 papers
3D vision · 57% Robot navigation and mapping · 25% Learning theory · 14%
Computer graphics and multimedia
2 papers
Geometric modeling and processing · 60% Image and video processing · 40%

Topics — the 10 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › motion estimation
camera motion estimation
0.011991
Camera stability problem and dynamic stereo vision · CVPR 1991
Robotics › Robot navigation and mapping
localization
0.011991
Camera stability problem and dynamic stereo vision · CVPR 1991
Geometric modeling and processing
3d reconstruction
0.011991
Camera stability problem and dynamic stereo vision · CVPR 1991
Machine learning › Learning theory
statistical pattern recognition
0.011988
Automatic Solder Joint Inspection · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Image and video processing
industrial inspection
0.011988
Automatic Solder Joint Inspection · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Computer vision › 3D vision
depth estimation
0.011987
Motion Stereo Using Ego-Motion Complex Logarithmic Mapping · IEEE Trans. Pattern Anal. Mach. Intell. 1987
Computer vision › 3D vision › motion estimation
ego-motion estimation
0.011987
Motion Stereo Using Ego-Motion Complex Logarithmic Mapping · IEEE Trans. Pattern Anal. Mach. Intell. 1987
Computer vision › 3D vision › stereo vision
motion stereo
0.011987
Motion Stereo Using Ego-Motion Complex Logarithmic Mapping · IEEE Trans. Pattern Anal. Mach. Intell. 1987
Knowledge, reasoning and agents › Knowledge representation and reasoning
expert systems
0.011988
Automatic Solder Joint Inspection · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Robotics › Robot navigation and mapping
mobile robot navigation
0.011987
Motion Stereo Using Ego-Motion Complex Logarithmic Mapping · IEEE Trans. Pattern Anal. Mach. Intell. 1987

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

stereo range data integration · 0.0image-registered range maps · 0.0voting scheme · 0.0statistical pattern recognition · 0.0expert system · 0.0dimensionality reduction · 0.0ego-motion complex logarithmic mapping · 0.0complex logarithmic mapping · 0.0
YearPublicationVenuePosition
1991 Camera stability problem and dynamic stereo vision
abstract
The authors show how stereo-based range data, obtained over time by a moving vehicle can be integrated without the explicit knowledge or computation of camera motion. A unique aspect of the approach is the transformation of range information into relative distances and encoding these distances in image registered maps. Results of experiments on dynamic stereo images, are presented.>
Saied Moezzi, Sandra L. Bartlett, Terry E. Weymouth
CVPR2
1988 Automatic Solder Joint Inspection
abstract
The task of automating the visual inspection of pin-in-hole solder joints is addressed. Two approaches are explored: statistical pattern recognition and expert systems. An objective dimensionality-reduction method is used to enhance the performance of traditional statistical pattern recognition approaches by decorrelating feature data, generating feature weights, and reducing run-time computations. The expert system uses features in a manner more analogous to the visual clues that a human inspector would rely on for classification. Rules using these cues are developed, and a voting scheme is implemented to accumulate classification evidence incrementally. Both methods compared favorably with human inspector performance.>
Sandra L. Bartlett, Paul J. Besl, Charles L. Cole, Ramesh Jain 0001, Debashish Mukherjee, Kurt D. Skifstad
IEEE Trans. Pattern Anal. Mach. Intell.1
1987 Motion Stereo Using Ego-Motion Complex Logarithmic Mapping
abstract
Stereo information can be obtained using a moving camera. If a dynamic scene is acquired using a translating camera and the camera motion parameters are known, then the analysis of the scene may be facilitated by ego-motion complex logarithmic mapping (ECLM). It is shown in this paper that by using the complex logarithmic mapping (CLM) with respect to the focus of expansion, the depth of stationary components can be determined easily in the transformed image sequence. The proposed approach for depth recovery avoids the difficult problems of establishing correspondence and computation of optical flow, by using the ego-motion information. An added advantage of the CLM will be the invariances it offers. We report our experiments with synthetic data to show the sensitivity of the depth recovery, and show results of real scenes to demonstrate the efficacy of the proposed motion stereo in applications such as autonomous navigation.
Ramesh Jain 0001, Sandra L. Bartlett, Nancy O'Brien
IEEE Trans. Pattern Anal. Mach. Intell.2