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VLDB 2026 Research / reviewers in the wild / expert

Deeptiman Jugessur

dblp:34/2371 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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
2 papers
Robot navigation and mapping · 46% Representation and self-supervised learning · 30% Image recognition and object detection · 23%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › object recognition
appearance-based object recognition		 0.012000
Local Appearance for Robust Object Recognition · CVPR 2000
Robotics › Robot navigation and mapping › place recognition
appearance-based place recognition		 0.012000
Robust Place Recognition using Local Appearance Based Methods · ICRA 2000
Robotics › Robot navigation and mapping
place recognition		 0.012000
Robust Place Recognition using Local Appearance Based Methods · ICRA 2000
Machine learning › Representation and self-supervised learning › representation learning › visual representation learning
visual feature learning		 0.012000
Robust Place Recognition using Local Appearance Based Methods · ICRA 2000
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
principal component analysis		 0.012000
Local Appearance for Robust Object Recognition · CVPR 2000

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

principal component analysis · 0.1statistical feature learning · 0.0attention mechanism · 0.0
YearPublicationVenuePosition
2000 Local Appearance for Robust Object Recognition
abstract
We present an approach to appearance-based object recognition using single camera images. Our approach is based on using an attention mechanism to obtain visual features that are generic, robust and informative. The features themselves are recognized using principal components an the frequency domain. In this paper we show how the visual characteristics of only a small number of such features can be used for appearance-based object recognition that is not confounded by planar rotations or background clutter.
Deeptiman Jugessur, Gregory Dudek
 CVPR1
2000 Robust Place Recognition using Local Appearance Based Methods
abstract
We present an approach to the automatic recognition of locations or landmarks using single camera images. Our approach is to learn visual features in the appearance domain that can be used to characterize an object or a location. These features are defined statistically and then are recognized using principal components in the frequency domain. We show that this technique can be used to recognize specific objects on varying backgrounds, as well as environmental features.
Gregory Dudek, Deeptiman Jugessur
 ICRA2