David Kaziska

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To develop statistical models for shapes, we utilize an elastic string representation where curves (denoting shapes) can bend and locally stretch (or compress) to optimally match each other, resulting in geodesic paths on shape spaces. We develop statistical models for capturing variability under the elastic-string representation. The basic idea is to(More)
PURPOSE There are multiple current strategies for breast radiotherapy (RT). The alignment of physician practice patterns with best evidence and patient preferences will enhance patient autonomy and improve cancer care. However, there is little information describing patient preferences for breast RT and physician practice patterns. METHODS AND MATERIALS(More)
We present a geometric and statistical approach to gaitbased human recognition. The novelty here is to consider observations of gait, considered as planar silhouettes, to be cyclostationary processes on a shape space of simple closed curves. Consequently, gait analysis reduces to quantifying differences between underlying stochastic processes using their(More)
We describe an approach for statistical analysis of shapes of closed curves using tools from differential geometry. This approach uses geodesic paths to define a metric on shape space, that is used to compare shapes, to compute intrinsic statistics for a set of shapes, and to define probability models on shape spaces. We demonstrate this approach using: (i)(More)
We study the problem of analyzing and classifying human gait by modeling it as a stochastic process on a shape space. We consider gait as a evolution of human silhouettes as seen in video sequences, and focus on their shapes. More specifically, we define a shape space of planar, closed curves and model a human gait as a stochastic process on this space. Due(More)
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