Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jih-Fang Wang

dblp:85/4194 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 1992
—ORCID · none

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

Artificial intelligence and machine learning · 2Graphics, 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
Geometric modeling and processing · 100%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d shape reconstruction
0.011992
Surface Reconstruction Using Deformable Models with Interior and Boundary Constraints · IEEE Trans. Pattern Anal. Mach. Intell. 1992
Computer vision › 3D vision › 3d reconstruction › non-rigid reconstruction
deformable surface reconstruction
0.011992
Surface Reconstruction Using Deformable Models with Interior and Boundary Constraints · IEEE Trans. Pattern Anal. Mach. Intell. 1992
Geometric modeling and processing › 3d reconstruction › non-rigid 3d reconstruction
deformable object reconstruction
0.011990
Surface reconstruction using deformable models with interior and boundary constraints · ICCV 1990
Geometric modeling and processing › deformable models
elastic deformable models
0.011990
Surface reconstruction using deformable models with interior and boundary constraints · ICCV 1990
Geometric modeling and processing
surface reconstruction
0.011990
Surface reconstruction using deformable models with interior and boundary constraints · ICCV 1990

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

least action principle · 0.0hamilton's principle · 0.0elastic grid model · 0.0hamilton principle · 0.0elastic grid · 0.0
YearPublicationVenuePosition
1992 Surface Reconstruction Using Deformable Models with Interior and Boundary Constraints
abstract
The authors introduce a technique for 3D surface reconstruction using elastic deformable-models. The model used is an imaginary elastic grid, which is made of membranous, thin-plate-type material. The elastic grid can bent, twisted, compressed, and stretched into any desired 3D shape, which is specified by the shape constraints derived automatically from images of a real 3D object. Shape reconstruction is guided by a set of imaginary springs that enforce the consistency in the position, orientation, and/or curvature measurements of the elastic grid and the desired shape. The dynamics of a surface reconstruction process is regulated by Hamilton's principle or the principle of the least action. Furthermore, a 1D deformable template that borders the elastic grid may be used. This companion boundary template is attracted/repelled by image forces to conform with the silhouette of the imaged object. Implementation results using simple analytic shapes and images of real objects are presented.>
Yuan-Fang Wang, Jih-Fang Wang
IEEE Trans. Pattern Anal. Mach. Intell.2
1990 Surface reconstruction using deformable models with interior and boundary constraints
abstract
A technique is introduced for 3-D surface reconstruction and graphic animation using elastic, deformable models. The basic structure used is an imaginary elastic grid, which is made of membranous, thin-plate type material. This elastic grid is bent, twisted, compressed, and stretched into any desirable 3-D shape, or from one flexible state to another. The desired shape can be specified by the shape constraints derived automatically from images of a real 3-D object, or by an analytic surface function. Shape reconstruction is guided by a set of imaginary springs that enforce the consistency in the position, orientation, and/or curvature measurements of the elastic grid and the desired shape. The dynamics of a reconstruction process is regulated by the Hamilton principle or the principle of the least action. Implementation results using simple analytic shapes and images of real free-form objects are presented. The authors believe that their model is widely applicable in many surface reconstruction and graphic animation processes.>
Yuan-Fang Wang, Jih-Fang Wang
ICCV2