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Yansong Jiang

dblp:381/6325 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
depth and reconstruction
0.912025
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
0.912025
Generalized Multi-Feature-Guided Progressive Fringe Order Correction for Fringe Projection Profilometry · IEEE Trans. Image Process. 2025
Computer vision › 3D vision
phase unwrapping
0.912025
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
YearPublicationVenuePosition
2025 Single-shot phase-shifting composition fringe projection profilometry by multi-attention fringe restoration network
Jiayi Qin, Yansong Jiang, Yiping Cao
Neurocomputing2
2025 Generalized Multi-Feature-Guided Progressive Fringe Order Correction for Fringe Projection Profilometry
abstract
Phase 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