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Jacob M. Gryn

dblp:18/4940 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2

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
Video understanding and tracking · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking
object tracking
0.112010
Visual Tracking Using a Pixelwise Spatiotemporal Oriented Energy Representation · ECCV (4) 2010
Image and video processing › image representation
spatiotemporal representation
0.112010
Visual Tracking Using a Pixelwise Spatiotemporal Oriented Energy Representation · ECCV (4) 2010

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

oriented energy filters · 0.2
YearPublicationVenuePosition
2010 Visual Tracking Using a Pixelwise Spatiotemporal Oriented Energy Representation
Kevin J. Cannons, Jacob M. Gryn, Richard P. Wildes
ECCV (4)2
2009 Detecting motion patterns via direction maps with application to surveillance
Jacob M. Gryn, Richard P. Wildes, John K. Tsotsos
Comput. Vis. Image Underst.1
2005 Three-dimensional nth derivative of Gaussian separable steerable filters
abstract
This paper details the construction of three-dimensional separable steerable filters. The approach presented is an extension of the construction of two-dimensional separable steerable filters outlined in W.T. Freeman and E.H. Adelson (1991). Additionally, three-dimensional separable steerable filters, both continuous and discrete versions, for the second derivative of the Gaussian and its Hilbert transform are reported. Experimental evaluation demonstrates that the errors in the constructed separable filters are negligible.
Konstantinos G. Derpanis, Jacob M. Gryn
ICIP (3)2