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Sundeep Gupta

dblp:66/8231 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1992
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, 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.

Computer graphics and multimedia
1 paper
Image and video processing · 77% Multimedia analysis and retrieval · 23%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
range image processing
0.011992
Edge detection in range images through morphological residue analysis · CVPR 1992
Image and video processing
edge detection
0.011992
Edge detection in range images through morphological residue analysis · CVPR 1992
Multimedia analysis and retrieval
image analysis
0.011992
Edge detection in range images through morphological residue analysis · CVPR 1992
Image and video processing › edge detection
morphological edge detection
0.011992
Edge detection in range images through morphological residue analysis · CVPR 1992
Image and video processing › edge detection
multiscale edge detection
0.011992
Edge detection in range images through morphological residue analysis · CVPR 1992
Image and video processing › edge analysis
edge classification
0.011992
Edge detection in range images through morphological residue analysis · CVPR 1992

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

residue analysis · 0.0mathematical morphology · 0.0
YearPublicationVenuePosition
1992 Edge detection in range images through morphological residue analysis
abstract
Two morphological methods for edge detection in range images are proposed. The first method uses the opening and closing residues of structuring elements in orthogonal directions to detect roof and crease edges, and is essentially a morphological implementation of residue analysis techniques. The more general second method is based on a morphological version of the first derivative operator. This method utilizes dilation and erosion residues of structuring elements at different scales to reliably extract step edges along with roof edges and crease edges, and to classify each pixel as belonging to eight possible structure types: positive roof, negative roof, positive crease, negative crease, top of step, base of step, ramp, and constant surface. This method may be thought of as a morphological multiscale method.>
Raghu Krishnapuram, Sundeep Gupta
CVPR2