Mark C. Allmen

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Recovering a hierarchical motion description of a long image sequence is one way to recognize objects and their motions. Intermediate-level and high-level motion analysis, i.e., recognizing a coordinated sequence of events such as walking and throwing, has been formulated previously as a process that follows high-level object recognition. This thesis(More)
To date, the overwhelming use of motion in computational vision has been to recover the three-dimensional structure of the scene. We propose that there are other, more powerful, uses for motion. Toward this end, we deene dynamic perceptual organization as an extension of the traditional (static) perceptual organization approach. Just as static perceptual(More)
We have developed a novel approach to the extraction of cloud base height (CBH) from pairs of whole-sky imagers (WSIs). The core problem is to spatially register cloud fields from widely separated WSIs; this complete, triangulation provides the CBH measurements. The wide camera separation and the self-similarity of clouds defeats standard matching(More)
Three-dimensional (3-D) cloud characterization permits the derivation of important cloud geometry properties such as fractional cloudiness, mean cloud and clear length, aspect ratio, and the morphology of cloud cover. These properties are needed as input to the hierarchical diagnosis (HD) and instantaneous radiative transfer (IRF) models, to validate(More)
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