Sean M. Culhane

dblp:40/19 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 1995
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 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
Deep learning architectures and training · 42% Image recognition and object detection · 35% 3D vision · 23%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › attention mechanism
visual attention
0.011995
Modeling Visual Attention via Selective Tuning · Artif. Intell. 1995
Computer vision › Image recognition and object detection
visual attention modeling
0.011995
Modeling Visual Attention via Selective Tuning · Artif. Intell. 1995
Computer vision › 3D vision
low-level vision
0.011992
An Attentional Prototype for Early Vision · ECCV 1992
Machine learning › Deep learning architectures and training
attention mechanism
0.011992
An Attentional Prototype for Early Vision · ECCV 1992

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

selective tuning · 0.0attentional prototype · 0.0
YearPublicationVenuePosition
1995 Modeling Visual Attention via Selective Tuning
John K. Tsotsos, Sean M. Culhane, Winky Yan Kei Wai, Yuzhong Lai, Neal Davis, Fernando Nuflo
Artif. Intell.2
1992 An Attentional Prototype for Early Vision
Sean M. Culhane, John K. Tsotsos
ECCV1
1992 A prototype for data-driven visual attention
abstract
Mounting evidence suggests that attentional mechanisms may be required to successfully perform many vision tasks. The paper presents an attentional prototype for early visual processing. The model is composed of a processing hierarchy and an attention beam that traverses the hierarchy, passing through the regions of greatest interest and inhibiting the regions that are not relevant. The type of input to the prototype is not limited to visual stimuli. Aspects of attention such as localizing spatial regions of interest and ordering their importance are addressed; other aspects of attention such as the role of task guidance are encompassed by the model but are not detailed here. Simulations using high-resolution digitized images were conducted, with oriented edge information as the input to the model. The results confirm that this prototype is both robust and fast, and promises to be essential to any real-time vision system.>
Sean M. Culhane, John K. Tsotsos
ICPR (1)1