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John W. Zweck

dblp:02/5480 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2001
—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 · 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
1 paper
Segmentation and scene understanding · 67% Image recognition and object detection · 33%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%
Theoretical computer science
1 paper
Computational geometry · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding › perceptual grouping
contour completion		 0.012001
A Rotation and Translation Invariant Discrete Saliency Network · NIPS 2001
Computer vision › Image recognition and object detection › object detection
contour detection		 0.012001
A Rotation and Translation Invariant Discrete Saliency Network · NIPS 2001
Computer vision › Segmentation and scene understanding
saliency network		 0.012001
A Rotation and Translation Invariant Discrete Saliency Network · NIPS 2001
Multimedia analysis and retrieval
image analysis		 0.012000
Euclidean Group Invariant Computation of Stochastic Completion Fields Using Shiftable-Twistable Functions · ECCV (2) 2000

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

shiftable-twistable functions · 0.1random process model · 0.0neural network · 0.0
YearPublicationVenuePosition
2001 A Rotation and Translation Invariant Discrete Saliency Network
abstract
We describe a neural network which enhances and completes salient closed contours. Our work is different from all previous work in three important ways. First, like the input provided to V1 by LGN, the in- put to our computation is isotropic. That is, the input is composed of spots not edges. Second, our network computes a well defined function of the input based on a distribution of closed contours characterized by a random process. Third, even though our computation is implemented in a discrete network, its output is invariant to continuous rotations and translations of the input pattern.
Lance R. Williams, John W. Zweck
 NIPS2
2000 Euclidean Group Invariant Computation of Stochastic Completion Fields Using Shiftable-Twistable Functions
John W. Zweck, Lance R. Williams
 ECCV (2)1
1999 Computing Stochastic Completion Fields in Linear-Time Using a Resolution Pyramid
Lance R. Williams, John W. Zweck, Tairan Wang, Karvel K. Thornber
 Comput. Vis. Image Underst.2