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Shenquan Qu

dblp:151/8867 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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.

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

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image restoration › document image restoration
document image rectification
0.212014
Active Flattening of Curved Document Images via Two Structured Beams · CVPR 2014

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

structured light · 0.2developable surface interpolation · 0.2
YearPublicationVenuePosition
2014 Active Flattening of Curved Document Images via Two Structured Beams
abstract
Document images captured by a digital camera often suffer from serious geometric distortions. In this paper, we propose an active method to correct geometric distortions in a camera-captured document image. Unlike many passive rectification methods that rely on text-lines or features extracted from images, our method uses two structured beams illuminating upon the document page to recover two spatial curves. A developable surface is then interpolated to the curves by finding the correspondence between them. The developable surface is finally flattened onto a plane by solving a system of ordinary differential equations. Our method is a content independent approach and can restore a corrected document image of high accuracy with undistorted contents. Experimental results on a variety of real-captured document images demonstrate the effectiveness and efficiency of the proposed method.
Gaofeng Meng, Ying Wang 0008, Shenquan Qu, Shiming Xiang, Chunhong Pan
CVPR3