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We develop a generative probabilistic model for temporally consistent super pixels in video sequences. In contrast to supermodel methods, object parts in different frames are tracked by the same temporal super pixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate super pixels in an online(More)
OBJECTIVE The etiology and pathogenesis of human inflammatory myopathies remain unclear. Findings of several studies suggest that the degree of inflammation does not correlate consistently with the severity of clinical disease or of structural changes in the muscle fibers, indicating that nonimmune pathways may contribute to the pathogenesis of myositis.(More)
We present a method for sampling from the posterior distribution of implicitly defined segmentations conditioned on the observed image. Segmentation is often formulated as an energy minimization or statistical inference problem in which either the optimal or most probable configuration is the goal. Exponentiating the negative energy functional provides a(More)
Directional data, naturally represented as points on the unit sphere, appear in many applications. However, unlike the case of Euclidean data, flexible mixture models on the sphere that can capture correlations, handle an unknown number of components and extend readily to high-dimensional data have yet to be suggested. For this purpose we propose a(More)
We present a novel representation for modeling textured regions subject to smooth variations in orientation and scale. Utilizing the steerable pyramid of Simoncelli and Freeman as a basis, we decompose textured regions of natural images into explicit local attributes of contrast, bias, scale, and orientation. Additionally, we impose smoothness on these(More)
We present an integrated probabilistic model for layered object tracking that combines dynamics on implicit shape representations, topological shape constraints, adaptive appearance models, and layered flow. The generative model combines the evolution of appearances and layer shapes with a Gaussian process flow and explicit layer ordering. Efficient MCMC(More)
This thesis project will focus on investigating extensions and improvements to the curve evolution and non-parametric density estimate based image segmentation approach proposed by Kim et al. [13]. One main problem with the algorithm proposed by Kim was that it was based solely on the individual scalar pixel intensities and did not take advantage of the(More)