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We present a content-aware image copy-and-paste technique which combines ideas from both matting and gradient-based methods. We modify the diffusion process used in the gradient-based approach to use the alpha matte for the cloned area as a weight function to control intensity interpolation. This ensures that the color style of the significant parts of the(More)
In this paper, we propose an articulated and generalized Gaussian kernel correlation (GKC)-based framework for human pose estimation. We first derive a unified GKC representation that generalizes the previous sum of Gaussians (SoG)-based methods for the similarity measure between a template and an observation both of which are represented by various SoG(More)
We present new multilayer joint gait-pose manifolds (multilayer JGPMs) for complex human gait motion modeling, where three latent variables are defined jointly in a low-dimensional manifold to represent a variety of body configurations. Specifically, the pose variable (along the pose manifold) denotes a specific stage in a walking cycle; the gait variable(More)
Using dark channel prior—a kind of statistics of the haze-free outdoor images—to remove haze from a single image input is simple and effective. However, due to the use of soft matting algorithm, the method suffers from massive consumption of both memory and time, which largely limits its scalability for large images. In this paper, we present a hierarchical(More)
Researchers often have access to a variety of different high-performance computer (HPC) systems in different administrative domains, possibly across a wide-area network. Consequently, the security infrastructure becomes an important component of an overlay metacomputer: a user-level aggregation of HPC systems. The Grid Security Infrastructure (GSI) uses a(More)
We present a new structure-guided joint gait pose manifold (JGPM) that represents gait kinematics by two variables. One is the pose to denote a series of stages in a walking cycle and the other is the gait to reflect the individual walking styles. Coupling pose and gait variables in the same latent space, such as a torus-like JGPM, was shown promising and(More)
We evaluate recent Gaussian process (GP)-based manifold learning methods for human motion modeling, including our recently proposed joint gait and pose manifolds (JGPMs). Unlike most GP algorithms that involve either one latent variable or multiple independent variables in separate latent spaces, JGPMs define two variables jointly and explicitly in one(More)
We propose a new point set registration method, Global-Local Topology Preservation (GLTP), which can cope with complex non-rigid transformations including highly articulated deformation. The registration is formulated as a Maximum Likelihood (ML) estimation problem with two topologically complementary constraints. The first is the previous Coherent Point(More)