Lip Posture Estimation using Kinematically Constrained Mixture Models

1998 ◽  
Author(s):  
P. H. Kelly ◽  
E. A. Hunter ◽  
K. Kreutz-Delgado ◽  
R. Jain
Keyword(s):  
Mixture Models ◽  
Constrained Mixture ◽  
Posture Estimation

Relevant subtask learning by constrained mixture models

2010 ◽  
Vol 14 (6) ◽  
pp. 641-662 ◽  
Author(s):  
Jaakko Peltonen ◽  
Yusuf Yaslan ◽  
Samuel Kaski
Keyword(s):  
Mixture Models ◽  
Constrained Mixture

scholarly journals Fast, Rate-Independent, Finite Element Implementation of a 3D Constrained Mixture Model of Soft Tissue Growth and Remodeling

2020 ◽  
Author(s):  
Marcos Latorre ◽  
Jay D. Humphrey
Keyword(s):  
Finite Element ◽  
Soft Tissue ◽  
Mixture Models ◽  
Tissue Growth ◽  
Aortic Aneurysms ◽  
Growth And Remodeling ◽  
Adaptive Process ◽  
Finite Element Implementation ◽  
Constrained Mixture ◽  
Diseased Segment

AbstractConstrained mixture models of soft tissue growth and remodeling can simulate many evolving conditions in health as well as in disease and its treatment, but they can be computationally expensive. In this paper, we derive a new fast, robust finite element implementation based on a concept of mechanobiological equilibrium that yields fully resolved solutions and allows computation of quasi-equilibrated evolutions when imposed perturbations are slow relative to the adaptive process. We demonstrate quadratic convergence and verify the model via comparisons with semi-analytical solutions for arterial mechanics. We further examine the enlargement of aortic aneurysms for which we identify new mechanobiological insights into factors that affect the nearby non-aneurysmal segment as it responds to the changing mechanics within the diseased segment. Because this new 3D approach can be implemented within many existing finite element solvers, constrained mixture models of growth and remodeling can now be used more widely.

Model-Based segmentation of image data using spatially constrained mixture models

2018 ◽  
Vol 283 ◽  
pp. 214-227 ◽  
Author(s):  
Can Hu ◽  
Wentao Fan ◽  
Jixiang Du ◽  
Yuchen Zeng
Keyword(s):  
Mixture Models ◽  
Image Data ◽  
Model Based ◽  
Constrained Mixture

scholarly journals Constrained mixture models in competing risks problems

1999 ◽  
Vol 10 (6) ◽  
pp. 753-767 ◽  
Author(s):  
S. K. Ng ◽  
G. J. McLachlan ◽  
D. C. McGiffin ◽  
M. F. O'Brien
Keyword(s):  
Mixture Models ◽  
Competing Risks ◽  
Constrained Mixture

scholarly journals Evaluation of fundamental hypotheses underlying constrained mixture models of arterial growth and remodelling

2009 ◽  
Vol 367 (1902) ◽  
pp. 3585-3606 ◽  
Author(s):  
A. Valentín ◽  
J. D. Humphrey
Keyword(s):  
Smooth Muscle ◽  
Smooth Muscle Cells ◽  
Mixture Models ◽  
Soft Tissues ◽  
Mass Density ◽  
Constitutive Relations ◽  
Muscle Cells ◽  
Combined Effects ◽  
Mechanical Stimuli ◽  
Constrained Mixture

Evolving constituent composition and organization are important determinants of the biomechanical behaviour of soft tissues. In arteries, vascular smooth muscle cells and fibroblasts continually produce and degrade matrix constituents in preferred modes and at altered rates in response to changing mechanical stimuli. Smooth muscle cells similarly exhibit vasoactive changes that contribute to the control of overall structure, function and mechanical behaviour. Constrained mixture models provide a useful framework in which to quantify arterial growth and remodelling for they can account for cell-mediated changes in individual structurally significant constituents. Our simulations show that the combined effects of changing mass density turnover and vasoactivity, as well as the prestretch at which constituents are incorporated within extant matrix, are essential to capture salient features of bounded arterial growth and remodelling. These findings emphasize the importance of formulating biologically motivated constitutive relations in any theory of growth and remodelling and distinct advantages of the constrained mixture approach, in particular.

Post Hoc Investigation of Factor Mixture Models

2007 ◽  
Author(s):  
Danielle L. Cisler ◽  
Gitta H. Lubke
Keyword(s):  
Mixture Models ◽  
Post Hoc
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