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Tanweer Kabir

dblp:142/4092 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1993
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 2Artificial intelligence and machine learning · 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.

Artificial intelligence
1 paper
Image recognition and object detection · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection
object recognition		 0.011993
Real-time recognition with the entire Brodatz texture database · CVPR 1993
Computer vision › Image recognition and object detection › efficient visual recognition
real-time recognition		 0.011993
Real-time recognition with the entire Brodatz texture database · CVPR 1993
Image and video processing
texture analysis		 0.011993
Real-time recognition with the entire Brodatz texture database · CVPR 1993
Image and video processing › texture analysis
texture classification		 0.011993
Real-time recognition with the entire Brodatz texture database · CVPR 1993

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

principal component analysis · 0.0multiscale autoregressive model · 0.0
YearPublicationVenuePosition
1993 Real-time recognition with the entire Brodatz texture database
abstract
The Brodatz Album has become the de facto standard for evaluating texture algorithms, with hundreds of studies having been applied to small sets of its images. The authors compare two powerful recognition algorithms, principal components analysis and multiscale autoregressive models, by evaluating them on a 999-image database derived from the entire Brodatz Album. The variety of homogeneous and nonhomogeneous images studied is thus nearly an order of magnitude larger than has been compared before, giving one snapshot of the state of the art in real-time texture recognition.>
Rosalind W. Picard, Tanweer Kabir, Fang Liu 0027
 CVPR2
1993 Finding similar patterns in large image databases
Rosalind W. Picard, Tanweer Kabir
 ICASSP (5)2