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Many of the computer vision algorithms have been posed in various forms of differential equations, derived from minimization of specific energy functionals, and the finite element representation and computation have become the de facto numerical strategies for solving these problems. However, for cases where domain mappings between numerical iterations or(More)
We propose a feature-based hierarchical framework for hand geometry recognition, based upon matching of geometrical and shape features. Rid of the needs for pegs, the acquisition of the hand images is simplified and more user-friendly. Geometrical significant landmarks are e x-tracted from the segmented images, and are used for hand alignment and the(More)
We propose and validate the hypothesis that we can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three-dimensional (3-D) image sequences (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the point correspondence maps. The noninvasive,(More)
Personalized noninvasive imaging of subject-specific cardiac electrical activity can guide and improve preventive diagnosis and treatment of cardiac arrhythmia. Compared to body surface potential (BSP) recordings and electrophysiological information reconstructed on heart surfaces, volumetric myocardial transmembrane potential (TMP) dynamics is of greater(More)
We present a model-based framework for imaging 3D cardiac transmembrane potential (TMP) distributions from body surface potential (BSP) measurements. Based on physiologically motivated modeling of the spatiotemporal evolution of TMPs and their projection to body surface, the cardiac electrophysiology is modeled as a stochastic system with TMPs as the latent(More)
Non–rigid motion estimation from image sequences is essential in analyzing and understanding the dynamic behaviors of physical objects. One important example is the dense field motion analysis of the cardiac wall, which could potentially help to better understand the physiological processes associated with heart disease and to provide improvement in patient(More)
Bayesian approaches, or maximum a posteriori (MAP) methods, are effective in providing solutions to ill-posed problems in image reconstruction. Based on Bayesian theory, prior information of the target image is imposed on image reconstruction to suppress noise. Conventionally, the information in most of prior models comes from weighted differences between(More)
Accurate estimation of heart wall dense field motion and deformation could help to better understand the physiological processes associated with ischemic heart diseases, and to provide significant improvement in patient treatment. We present a new method of estimating left ventricular deformation which integrates instantaneous velocity information obtained(More)