despite recent advances in video inpainting techniques, reconstructing large missing regions of a moving subject while its scale changes remains an elusive goal. in this paper, we have introduced a scale-change invariant method for large missing regions to tackle this problem. using this framework, first the moving foreground is separated from the background and its scale is equalized. then, a ...

background: increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. methods: in this paper, manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images in two dimension...

Manifold Learning learns a low-dimensional embedding of the latent manifold. In this report, we give the definition of distance metric learning, provide the categorization of manifold learning, and describe the essential connection between manifold learning and distance metric learning, with special emphasis on nonlinear manifold learning, including ISOMAP, Laplacian Eigenamp (LE), and Locally ...

We propose a simple method to identify continuous Lie algebra symmetry in dataset through regression by an artificial neural network. Our proposal takes advantage of the $\mathcal{O}({\ensuremath{\epsilon}}^{2})$ scaling output variable under infinitesimal transformations on input variables. As are generated post-training, methodology does not rely sampling full representation space or binning ...

Recently manifold structures have attracted attentions in two folds in the machine learning literature. One is in the manifold learning problem, that is learning the intrinsic manifold structure in high dimensional datasets. Another is in the information geometric approach to learning – exploiting the geometry of the parameter space of learning machines such as neural networks for improving con...