Paper Info
Title
Incremental update of linear appearance models and its application to AAM: incremental AAM
Abstract
Because many model-based object representation approaches such as active appearance models (AAMs) use a fixed linear appearance model, they often fail to fit to a novel image that is captured in a different imaging condition from that of training images. To alleviate this problem, we propose to use adaptive linear appearance model that is updated by the incremental principal component analysis (PCA). Because the incremental update algorithm uses a new appearance data that is obtained in an on-line manner, a reliable method to measure the quality of the new data is required not to break the integrity of the appearance model. For this purpose, we modified the adaptive observation model (AOM), which has been used to model the varying appearance of the target object using statistical model such as Gaussian mixtures. Experiment results showed that the incremental AAM that uses adaptive linear appearance model greatly improved the robustness to the varying illumination condition when compared to the traditional AAM.
Year
DOI
Venue
2007
10.1007/978-3-540-74260-9_48
ICIAR
Keywords
Field
DocType
fixed linear appearance model,incremental aam,new appearance data,active appearance model,varying appearance,statistical model,appearance model,incremental update,adaptive linear appearance model,incremental principal component analysis,adaptive observation model
Computer vision,Anomaly detection,Pattern recognition,Computer science,Active appearance model,Robustness (computer science),Gaussian,Artificial intelligence,Statistical model,Principal component analysis,Mixture model
Conference
Volume
ISSN
ISBN
4633
0302-9743
3-540-74258-1
Citations 
PageRank 
References 
3
0.42
10
Authors
3
Name
Order
Citations
PageRank
Sangjae Lee157342.48
Jaewon Sung21529.57
Daijin Kim31882126.85