Demo Data from EDBT 2026 / reviewers in the wild / expert
Yansong Jiang
dblp:381/6325
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, 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.
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
depth and reconstruction |
0.9 | 1 | 2025 | Generalized Multi-Feature-Guided Progressive Fringe Order Correction for Fringe Projection Profilometry · IEEE Trans. Image Process. 2025 |
Computer vision › 3D vision › 3d shape acquisition
fringe projection profilometry |
0.9 | 1 | 2025 | Generalized Multi-Feature-Guided Progressive Fringe Order Correction for Fringe Projection Profilometry · IEEE Trans. Image Process. 2025 |
Computer vision › 3D vision
phase unwrapping |
0.9 | 1 | 2025 | Generalized Multi-Feature-Guided Progressive Fringe Order Correction for Fringe Projection Profilometry · IEEE Trans. Image Process. 2025 |
Methods — techniques the papers use, named apart from their topics
multi-feature-guided progressive order correction · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Single-shot phase-shifting composition fringe projection profilometry by multi-attention fringe restoration network
Jiayi Qin, Yansong Jiang, Yiping Cao |
Neurocomputing | 2 |
| 2025 | Generalized Multi-Feature-Guided Progressive Fringe Order Correction for Fringe Projection ProfilometryabstractPhase unwrapping is a critical step in fringe projection profilometry, essential for achieving accurate and efficient three-dimension (3D) imaging. Temporal phase unwrapping is the most widely utilized to improve robustness and the reconstruction quality. Unfortunately, due to abrupt phase discontinuities at boundaries, misalignment between the wrapped phases, and unreliable shadow regions, fringe order errors may occur. To address these challenges, this study presents a generalized multi-feature-guided progressive order correction algorithm (GMP-OCA) for high-quality 3D imaging. The algorithm integrates global coarse detection, incremental line-wise optimization, and regional precision scanning to progressively correct fringe orders. Static and dynamic experimental results demonstrate that GMP-OCA effectively eliminates the systematic errors inherent in various phase unwrapping methods, producing high-quality 3D imaging results. Jiayi Qin, Yansong Jiang, Yiping Cao |
IEEE Trans. Image Process. | 2 |