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C. C. Laan

dblp:190/7217 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

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

Artificial 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
Segmentation and scene understanding · 50% Image recognition and object detection · 25% Generative modeling · 25%

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

TopicWeightPapersLastEvidence papers
Computer vision › Segmentation and scene understanding › image segmentation › document image segmentation
character segmentation
0.212016
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016
Computer vision › Segmentation and scene understanding
instance segmentation
0.212016
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016
Machine learning › Generative modeling › 3d generative model
mesh generative model
0.212016
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016
Computer vision › Image recognition and object detection
scene text recognition
0.212016
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data · NIPS 2016

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

generative modeling · 0.2
YearPublicationVenuePosition
2016 Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
abstract
We demonstrate that a generative model for object shapes can achieve state of the art results on challenging scene text recognition tasks, and with orders of magnitude fewer training images than required for competing discriminative methods. In addition to transcribing text from challenging images, our method performs fine-grained instance segmentation of characters. We show that our model is more robust to both affine transformations and non-affine deformations compared to previous approaches.
Xinghua Lou, Ken Kansky, Wolfgang Lehrach, C. C. Laan, Bhaskara Marthi, D. Scott Phoenix, Dileep George
NIPS4