TY - GEN
T1 - Elastically adaptive deformable models
AU - Metaxas, Dimitri
AU - Kakadiaris, Ioannis A.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1996.
PY - 1996
Y1 - 1996
N2 - We present a novel technique for the automatic adaptation of a deformable model's elastic parameters within a Kalman filter framework for shape estimation applications. The novelty of the technique is that the model's elastic parameters are not constant, but time varying. The model for the elastic parameter variation depends on the local error of fit and the rate of change of the error of fit. By augmenting the state equations of an extended Kalman filter to incorporate these additional variables and take into account the noise in the data, we are able to significantly improve the quality of the shape estimation. Therefore, the model's elastic parameters are initialized always to the same value and they subsequently modified depending on the data and the noise distribution. In addition, we demonstrate how this technique can be parallelized in order to increase its efficiency. We present several experiments to demonstrate the effectiveness of our method.
AB - We present a novel technique for the automatic adaptation of a deformable model's elastic parameters within a Kalman filter framework for shape estimation applications. The novelty of the technique is that the model's elastic parameters are not constant, but time varying. The model for the elastic parameter variation depends on the local error of fit and the rate of change of the error of fit. By augmenting the state equations of an extended Kalman filter to incorporate these additional variables and take into account the noise in the data, we are able to significantly improve the quality of the shape estimation. Therefore, the model's elastic parameters are initialized always to the same value and they subsequently modified depending on the data and the noise distribution. In addition, we demonstrate how this technique can be parallelized in order to increase its efficiency. We present several experiments to demonstrate the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=84957879904&partnerID=8YFLogxK
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U2 - 10.1007/3-540-61123-1_169
DO - 10.1007/3-540-61123-1_169
M3 - Conference contribution
AN - SCOPUS:84957879904
SN - 3540611231
SN - 9783540611233
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 550
EP - 559
BT - Computer Vision – ECCV 1996 - 4th European Conference on Computer Vision, Proceedings
A2 - Buxton, Bernard
A2 - Cipolla, Roberto
PB - Springer-Verlag
T2 - 4th European Conference on Computer Vision, ECCV 1996
Y2 - 15 April 1996 through 18 April 1996
ER -