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Weiduo Hu

dblp:157/3088 · DBLP profile ↗
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5ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0003-0090-3675ORCID · corroborated

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

Artificial intelligence and machine learning · 4Graphics, 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
Computational photography and imaging · 50% Geometric modeling and processing · 50%
Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › tomographic imaging
computed tomography
0.312018
Robust Virtual Unrolling of Historical Parchment XMT Images · IEEE Trans. Image Process. 2018
Geometric modeling and processing › surface processing
surface segmentation
0.312018
Robust Virtual Unrolling of Historical Parchment XMT Images · IEEE Trans. Image Process. 2018
Algorithmic game theory and mechanism design › matching
blossom algorithm
0.112018
Robust Virtual Unrolling of Historical Parchment XMT Images · IEEE Trans. Image Process. 2018

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

ink projection · 0.7global optimization · 0.7dynamic programming · 0.7
YearPublicationVenuePosition
2019 3D object recognition from cluttered and occluded scenes with a compact local feature
Wulong Guo, Weiduo Hu, Chang Liu 0009
Mach. Vis. Appl.2
2019 Real-time geometric fitting and pose estimation for surface of revolution
Chang Liu 0009, Weiduo Hu
Pattern Recognit.2
2018 Robust Virtual Unrolling of Historical Parchment XMT Images
abstract
We develop a framework to virtually unroll fragile historical parchment scrolls, which cannot be physically unfolded via a sequence of X-ray tomographic slices, thus providing easy access to those parchments whose contents have remained hidden for centuries. The first step is to produce a topologically correct segmentation, which is challenging as the parchment layers vary significantly in thickness, contain substantial interior textures and can often stick together in places. For this purpose, our method starts with linking the broken layers in a slice using the topological structure propagated from its previous processed slice. To ensure topological correctness, we identify fused regions by detecting junction sections, and then match them using global optimization efficiently solved by the blossom algorithm, taking into account the shape energy of curves separating fused layers. The fused layers are then separated using as-parallel-as-possible curves connecting junction section pairs. To flatten the segmented parchment, pixels in different frames need to be put into alignment. This is achieved via a dynamic programming-based global optimization, which minimizes the total matching distances and penalizes stretches. Eventually, the text of the parchment is revealed by ink projection. We demonstrate the effectiveness of our approach using challenging real-world data sets, including the water damaged fifteenth century Bressingham scroll.
Chang Liu 0009, Paul L. Rosin, Yukun Lai, Weiduo Hu
IEEE Trans. Image Process.4
2015 Effective ellipse detector with polygonal curve and likelihood ratio test
abstract
A robust ellipse detector is proposed. The detector preprocesses the edge map by removing all the isolated points and conjunction points, and exploits polygonal curve to extract the elliptical arcs. A non‐iterative geometric distance computation method is presented to serve a criterion which identifies the elliptical arcs belonging to the same ellipse by likelihood ratio test and fit ellipses to those merged arcs. The authors test their algorithm on both synthetic and real images, and the experimental results show a good performance of their algorithm.
Weiduo Hu, Chang Liu 0009, Daguang Yang
IET Comput. Vis.2
2015 Ellipse fitting for imaged cross sections of a surface of revolution
Chang Liu 0009, Weiduo Hu
Pattern Recognit.2