Paper Info
Title
Multi-level approach for statistical appearance models with probabilistic correspondences.
Abstract
Statistical shape and appearance models are often based on the accurate identification of one-to-one correspondences in a training data set. At the same time, the determination of these corresponding landmarks is the most challenging part of such methods. Hufnagel ct al.(1) developed an alternative method using correspondence probabilities for a statistical shape model. In Kruger ct al.(2,3) we propose the use of probabilistic correspondences for statistical appearance models by incorporating appearance information into the framework. We employ a point-based representation of image data combining position and appearance information. The model is optimized and adapted by a maximum a-posteriori (MAP) approach deriving a single global optimization criterion with respect to model parameters and observation dependent parameters, that directly affects shape and appearance information of the considered structures. Because initially unknown correspondence probabilities are used and a higher number of degrees of freedom is introduced to the model a regularization of the model generation process is advantageous. For this purpose we extend the derived global criterion by a regularization term which penalizes implausible topological changes. Furthermore, we propose a multi-level approach for the optimization, to increase the robustness of the model generation process.
Year
DOI
Venue
2016
10.1117/12.2214885
Proceedings of SPIE
Field
DocType
Volume
Training set,Computer vision,Active shape model,Pattern recognition,Global optimization,Robustness (computer science),Active appearance model,Regularization (mathematics),Artificial intelligence,Probabilistic logic,Physics
Conference
9784
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
4
3
Name
Order
Citations
PageRank
Julia Krüger1167.63
Jan Ehrhardt238754.33
Heinz Handels31527239.84