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Simulation Algorithms for Spatial Pattern Recognition
Phone: () -
Email: JACQUEZ@BIOMEDWARE.COM
DESCRIPTION (provided by applicant): A new generation of satellites is imaging
the earth's surface with unprecedented spatial and spectral resolution. With
the ability to identify local features related to environmental exposures, this
high-resolution imagery is gong to revolutionize health risk assessment. The
realization of this potential depends critically on our ability to recognize
spatial patterns on these large images. This project will develop fast spatial
null models for use in statistical pattern recognition, and will accomplish 4
aims.
(1) Implement fast simulation algorithms conditioned on properties of the data,
and on spatial functions;
(2) Assess project feasibility by evaluating the performance of these
algorithms on existing high-resolution, hyperspectral imagery;
(3) Implement the simulation algorithms in 2 commercial spatial analysis
software packages;
(4) Apply the software and methods to demonstrate the approach and unique
benefits for risk assessment.
The phase 1 research will address the first two aims; aims three and four will
be accomplished in phase 2 once feasibility is demonstrated. The technologic
and scientific innovations from this project are expected to greatly enhance
our ability to extract knowledge from high resolution imagery.
PROPOSED COMMERCIAL APPLICATION:
The imminent launch of over a dozen satellites capable of high-resolution imagery is giving
health researchers powerful new data for relating environmental features to health
outcomes, but existing software packages cannot undertake spatial analysis of these
extraordinarly large data sets. The fast simulation algorithms from this research will
be incorporated into 2 commercial software packages, providing advanced spatial
analysis for large imagery.
* Information listed above is at the time of submission. *