Skip to main navigation
Skip to search
Skip to main content
Technical University of Munich Home
Help & FAQ
English
Deutsch
Home
Profiles
Research units
Projects
Research output
Datasets
Prizes
Activities
Press/Media
Impacts
Search by expertise, name or affiliation
Dynamical statistical shape priors for level set-based tracking
Daniel Cremers
IEEE
University of Bonn
Research output
:
Contribution to journal
›
Article
›
peer-review
234
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Dynamical statistical shape priors for level set-based tracking'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Set-based
100%
Level-set
100%
Statistical Shape Prior
100%
Shape Prior
75%
Deformable Objects
50%
Occlusion
25%
Bayesian Framework
25%
Segmentation Method
25%
With Memory
25%
Noise Level
25%
Segmentation Accuracy
25%
Temporal Correlation
25%
Statistical Model
25%
Shape Knowledge
25%
Deformation Model
25%
Statistical Shape
25%
Joint Model
25%
Walking Human
25%
Familiar Objects
25%
Image Sequence Segmentation
25%
Computer Science
Statistical Shape
100%
Deformable Object
66%
Image Sequence
33%
segmentation accuracy
33%
Familiar Object
33%
Temporal Correlation
33%
Segmentation Method
33%
Bayesian Framework
33%
Engineering
Level Set
100%
Joints (Structural Components)
25%
Bayesian Framework
25%
Frame Rate
25%
Temporal Correlation
25%
Based Segmentation Method
25%
Noise Level
25%
Image Sequence
25%
Mathematical Model
25%
Mathematics
Level Set
100%
Bayesian
25%
Temporal Correlation
25%
Physics
Mathematical Model
100%