A framework for spatiotemporal control in the tracking of visual contours
International Journal of Computer Vision
Active shape models—their training and application
Computer Vision and Image Understanding
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Real-time active shape models for segmentation of 3D cardiac ultrasound
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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In this paper we present a framework for real-time tracking of deformable contours in volumetric datasets. The framework supports composite deformation models, controlled by parameters for contour shape in addition to global pose. Tracking is performed in a sequential state estimation fashion, using an extended Kalman filter, with measurement processing in information space to effectively predict and update contour deformations in real-time. A deformable B-spline surface coupled with a global pose transform is used to model shape changes of the left ventricle of the heart. Successful tracking of global motion and local shape changes without user intervention is demonstrated on a dataset consisting of 21 3D echocardiography recordings. Real-time tracking using the proposed approach requires a modest CPU load of 13% on a modern computer. The segmented volumes compare to a semi-automatic segmentation tool with 95% limits of agreement in the interval 4.1 ± 24.6 ml (r = 0.92).