We present Genie Pro, a new software tool for image analysis produced by the ISIS (Intelligent Search in Images and Signals) group at Los Alamos National Laboratory. Like the earlier GENIE tool produced by the same group, Genie Pro is a general purpose adaptive tool that derives automatic pixel classification algorithms for satellite/aerial imagery, from training input provided by a human expert. Genie Pro is a complete rewrite of our earlier work that incorporates many new ideas and concepts. In particular, the new software integrates spectral information; and spatial cues such as texture, local morphology and large-scale shape information; in a much more sophisticated way. In addition, attention has been paid to how the human expert interacts with the software: Genie Pro facilitates highly efficient training through an interactive and iterative "training dialog". Finally, the new software runs on both Linux and Windows platforms, increasing its versatility. We give detailed descriptions of the new techniques and ideas in Genie Pro, and summarize the results of a recent evaluation of the software.
Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation wide-field synoptic surveys are poised to revolutionize our understanding of just about anything that goes bump in the night (which is just about everything at some level). Still, even the most ambitious surveys will require targeted spectroscopic follow-up to fill in the physical details of newly discovered transients. We are now building a new system intended to ingest and classify transient phenomena in near real-time from high-throughput imaging data streams. Described herein, the Transient Classification Project at Berkeley will be making use of classification techniques operating on "features" extracted from time series and contextual (static) information. We also highlight the need for a community adoption of a standard representation of astronomical time series data (ie. "VOTimeseries").
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