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Il Y. Kim

dblp:36/5766 · DBLP profile ↗
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4ranked-venue papers
4as first author
0since 2021 · last 1996
—ORCID · none

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

Artificial intelligence and machine learning · 4 · 4 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 · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding
image segmentation		 0.011996
An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model · IEEE Trans. Pattern Anal. Mach. Intell. 1996
Computer vision › Segmentation and scene understanding › image segmentation › multi-task segmentation
joint image segmentation and labeling		 0.011996
An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model · IEEE Trans. Pattern Anal. Mach. Intell. 1996
Computer vision › Segmentation and scene understanding › image segmentation › probabilistic segmentation
markov random field segmentation		 0.011996
An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model · IEEE Trans. Pattern Anal. Mach. Intell. 1996

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

markov random field · 0.0energy minimization · 0.0
YearPublicationVenuePosition
1996 An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model
abstract
This paper presents a unified approach for the image understanding problem based on the Markov random field models. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process so that the erroneous segmentation and misinterpretation can be compensatedly recovered by continuous estimation of the unified energy function.
Il Y. Kim, Hyun Seung Yang
 IEEE Trans. Pattern Anal. Mach. Intell.1
1995 An integrated approach for scene understanding based on Markov Random Field model
Il Y. Kim, Hyun Seung Yang
 Pattern Recognit.1
1994 A systematic way for region-based image segmentation based on Markov Random Field model
Il Y. Kim, Hyun Seung Yang
 Pattern Recognit. Lett.1
1993 Efficient image labeling based on markov random field and error backpropagation network
Il Y. Kim, Hyun Seung Yang
 Pattern Recognit.1