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Jennell Vick

dblp:127/8803 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Graphics, 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 · 33% Visualization and visual analytics · 33% Image and video processing · 33%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

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

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › shape deformation
deformation analysis
0.212013
Physics-Based Deformable Tongue Visualization · IEEE Trans. Vis. Comput. Graph. 2013
Visualization and visual analytics
scientific visualization
0.212013
Physics-Based Deformable Tongue Visualization · IEEE Trans. Vis. Comput. Graph. 2013
Image and video processing › image segmentation › 3d image segmentation
volume segmentation
0.212013
Physics-Based Deformable Tongue Visualization · IEEE Trans. Vis. Comput. Graph. 2013

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

strain energy visualization · 0.3motion capture · 0.3finite element method · 0.3
YearPublicationVenuePosition
2013 Physics-Based Deformable Tongue Visualization
abstract
In this paper, a physics-based framework is presented to visualize the human tongue deformation. The tongue is modeled with the Finite Element Method (FEM) and driven by the motion capture data gathered during speech production. Several novel deformation visualization techniques are presented for in-depth data analysis and exploration. To reveal the hidden semantic information of the tongue deformation, we present a novel physics-based volume segmentation algorithm. This is accomplished by decomposing the tongue model into segments based on its deformation pattern with the computation of deformation subspaces and fitting the target deformation locally at each segment. In addition, the strain energy is utilized to provide an intuitive low-dimensional visualization for the high-dimensional sequential motion. Energy-interpolation-based morphing is also equipped to effectively highlight the subtle differences of the 3D deformed shapes without any visual occlusion. Our experimental results and analysis demonstrate the effectiveness of this framework. The proposed methods, though originally designed for the exploration of the tongue deformation, are also valid for general deformation analysis of other shapes.
Yin Yang 0002, Xiaohu Guo, Jennell Vick, Luis G. Torres, Thomas F. Campbell
IEEE Trans. Vis. Comput. Graph.3