Demo Data from VLDB 2026 / reviewers in the wild / expert
Wenquan Ming
dblp:316/9623
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-8845-7715ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
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 · 56% Computational photography and imaging · 44% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational photography and imaging › tomographic imaging
computed tomography |
0.6 | 1 | 2022 | IDART: An Improved Discrete Tomography Algorithm for Reconstructing Images With Multiple Gray Levels · IEEE Trans. Image Process. 2022 |
Image and video processing › image reconstruction › tomographic reconstruction
discrete tomography |
0.6 | 1 | 2022 | IDART: An Improved Discrete Tomography Algorithm for Reconstructing Images With Multiple Gray Levels · IEEE Trans. Image Process. 2022 |
Image and video processing
image reconstruction |
0.2 | 1 | 2022 | IDART: An Improved Discrete Tomography Algorithm for Reconstructing Images With Multiple Gray Levels · IEEE Trans. Image Process. 2022 |
Methods — techniques the papers use, named apart from their topics
modal value estimation · 0.6connected component labeling · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | IDART: An Improved Discrete Tomography Algorithm for Reconstructing Images With Multiple Gray LevelsabstractThe discrete algebraic reconstruction technique has many advantages in computed tomography and electron tomography. However, the number of gray levels and the absolute gray values that should be known in advance are typically not available in experiments especially when there are many gray levels in the image. In this paper, we report an automatic discrete tomography reconstruction algorithm to improve its feasibility in practice, without needing to know these two parameters. In our algorithm, the number of gray levels is estimated by labeling the connected components in the tomogram and the absolute values of them are determined by the modal value of each domain. The proposed algorithm was extensively validated on both simulated and experimental datasets. The results show that our algorithm can accurately recover not only the morphology but also the gray levels of the interested objects, even in the images with multiple gray levels. It is demonstrated that the presented algorithm is robust for eliminating missing wedge artifacts and tolerable for noisy data. Yutao He, Wenquan Ming, Ruohan Shen, Jianghua Chen |
IEEE Trans. Image Process. | 2 |