[1990] Proceedings Computers in Cardiology
DOI: 10.1109/cic.1990.144302
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A visual framework for the study of cardiac motion

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Cited by 12 publications
(8 citation statements)
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“…A possible 3D expansion is suggested taking similar considerations in to account [16]. However, similar work of Demi et al [17] implements regularization of the flow vectors using an interpolation scheme to estimate the shape correspondence between subsampled shape features. Experimental methods using physically implanted markers to quantify the motion in animal heart are presented in [18] where the markers were corresponded and tracked in a stereo static external co-ordinate system.…”
Section: Shape Correspondence Based Motion Estimationmentioning
confidence: 99%
“…A possible 3D expansion is suggested taking similar considerations in to account [16]. However, similar work of Demi et al [17] implements regularization of the flow vectors using an interpolation scheme to estimate the shape correspondence between subsampled shape features. Experimental methods using physically implanted markers to quantify the motion in animal heart are presented in [18] where the markers were corresponded and tracked in a stereo static external co-ordinate system.…”
Section: Shape Correspondence Based Motion Estimationmentioning
confidence: 99%
“…Additional efforts in 2-D tracking related to the frame-toframe approach have been proposed [12]- [14]. The first two approaches differ in the term used for regularization of the flow vectors.…”
Section: B Related Efforts In Image Sequence Analysismentioning
confidence: 99%
“…The first two approaches differ in the term used for regularization of the flow vectors. Additionally, [12] uses an interpolation scheme to quantify shape between subsampled shape features. The work in [13] also optimizes a cost functional over the space of the contour, however the resulting minimization is highly nonlinear and significantly more complex due to the requirement for mapping shape-based contour correspondences back into Euclidean space values to compute the smoothing term of the functional.…”
Section: B Related Efforts In Image Sequence Analysismentioning
confidence: 99%
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