Johnson and Hebert's spin-images have been applied to the registration of range images and object recognition with much success because they are rotation, scale, and pose invariant. In this paper we address two issues concerning spin-images, namely: (1) comparing uncompressed spin-images across large datasets is costly, and (2) a method to select the… (More)
Sensors such as video surveillance and weather monitoring systems record a significant amount of dynamic data which are represented by vector fields. We present a novel algorithm to measure the similarity of vector fields using global distributions that capture both vector field properties (e.g., vector orientation) and relational geometric information… (More)
Vector fields may come from video data (via optical flow and tracking), from weather phenomena (e.g., wind speed and direction), and from medical imaging. An important component in analyzing this data is to be able to quantitatively compare different points within a vector field or across different fields of the same type. We present a novel local… (More)
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We describe global and local methods for comparing flow fields, and a visualization tool that allows the user to adjust how flow fields are compared. Our first global method operates on path-lines and measures variations in orientation and curvature between samples on the same path-line. We then apply the global approach to first-order attributes computed… (More)
We address the problem of tracking points in dense vector fields. Such vector fields may come from computational fluid dynamics simulations, environmental monitoring sensors , or dense point tracking of video data. To track points in vector fields, we capture the distribution of higher-order properties (e.g., properties derived from the gradient of the… (More)