Photo stream segmentation is to segment photo streams into groups, each of which corresponds to an event. Photo
stream segmentation can be done with or without prior knowledge of event structure. In this paper, we study the problem
by assuming that there is no a priori event model available. Although both context and content information are important
for photo stream segmentation, we focus on investigating the usage of context information in this work. We consider
different information components of context such as time, location, and optical setting for inexpensive segmentation of
photo streams from common users of modern digital camera. As events are hierarchical, we propose to segment photo
stream using hierarchical mixture model. We compare the generated hierarchy with that created by users to see how well
results can be obtained without knowing the prior event model. We experimented with about 3000 photos from amateur
photographers to study the efficacy of the approach for these context information components.
The Electrooptic Beam Scanner is a solid state device developed at Carnegie Mellon's Data Storage Systems Center (DSSC) that is capable of scanning a laser beam approximately +/- 1 - 3 degree(s) at frequencies of at least 200 kHz. The optical group of the DSSC is currently employing the scanner as a fine actuator for high performance optical data storage systems. This paper discusses several aspects of that research, including multi-track scanning for increased data rate, controller for two stage actuator systems, and feedforward control for ultra-fast seeks.
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