TY - GEN
T1 - Active part-decomposition, shape and motion estimation of articulated objects
T2 - Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
AU - Kakadiaris, Ioannis A.
AU - Metaxas, Dimitri
AU - Bajcsy, Ruzena
PY - 1994
Y1 - 1994
N2 - We present a novel, robust, integrated approach to segmentation shape and motion estimation of articulated objects. Initially, we assume the object consists of a single part, and we fit a deformable model to the given data using our physics-based framework. As the object attains new postures, we decide based on certain criteria if and when to replace the initial model with two new models. These criteria are based on the model's state and the given data. We then fit the models to the data using a novel algorithm for assigning forces from the data to the two models, which allows partial overlap between them and determination of joint location. This approach is applied iteratively until all the object's moving parts are identified. Furthermore, we define new global deformations and we demonstrate our technique in a series of experiments, where Kalman filtering is employed to account for noise and occlusion.
AB - We present a novel, robust, integrated approach to segmentation shape and motion estimation of articulated objects. Initially, we assume the object consists of a single part, and we fit a deformable model to the given data using our physics-based framework. As the object attains new postures, we decide based on certain criteria if and when to replace the initial model with two new models. These criteria are based on the model's state and the given data. We then fit the models to the data using a novel algorithm for assigning forces from the data to the two models, which allows partial overlap between them and determination of joint location. This approach is applied iteratively until all the object's moving parts are identified. Furthermore, we define new global deformations and we demonstrate our technique in a series of experiments, where Kalman filtering is employed to account for noise and occlusion.
UR - http://www.scopus.com/inward/record.url?scp=0027963749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027963749&partnerID=8YFLogxK
U2 - 10.1109/cvpr.1994.323938
DO - 10.1109/cvpr.1994.323938
M3 - Conference contribution
AN - SCOPUS:0027963749
SN - 0818658274
SN - 9780818658273
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 980
EP - 984
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 June 1994 through 23 June 1994
ER -