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Displaying 1-7 out of 7 total
Interactive Machine Learning in Data Exploitation
Found in: Computing in Science & Engineering
By Reid Porter,James Theiler,Don Hush
Issue Date:September 2013
pp. 12-20
The goal of interactive machine learning is to help scientists and engineers exploit more specialized data from within their deployed environment in less time, with greater accuracy and fewer costs. A basic introduction to the main components is provided h...
 
Evaluating and improving local hyperspectral anomaly detectors
Found in: Applied Image Pattern Recognition Workshop,
By Leonardo R. Bachega,James Theiler,Charles A. Bouman
Issue Date:October 2011
pp. 1-8
This paper addresses two issues related to the detection of hyperspectral anomalies. The first issue is the evaluation of anomaly detector performance even when labeled data is not available. The second issue is the estimation of the covariance structure o...
 
Detection of ephemeral changes in sequences of images
Found in: Applied Image Pattern Recognition Workshop,
By James Theiler, Steven M. Adler-Golden
Issue Date:October 2008
pp. 1-8
The formalism of anomalous change detection, which was developed for finding unusual changes in pairs of images, is extended to sequences of more than two images. Extended algorithms based on RX, Chronochrome, and Hyper are presented for identifying the mo...
 
Subpixel Anomalous Change Detection in Remote Sensing Imagery
Found in: Image Analysis and Interpretation, IEEE Southwest Symposium on
By James Theiler
Issue Date:March 2008
pp. 165-168
A machine-learning framework for anomalous change detection is extended to the situation in which the anomalous change is smaller than a pixel. Although the existing framework can be applied to (and does have power against) the subpixel case, it is possibl...
 
Approach to Target Detection based on Relevant Metric for Scoring Performance
Found in: Applied Image Pattern Recognition Workshop,
By James Theiler, Neal Harvey, Nancy A. David, John M. Irvine
Issue Date:October 2004
pp. 184-189
Improved target detection, reduced false alarm rates, and enhanced timeliness are critical to meeting the requirements of current and future military missions. We present a new approach to target detection, based on a suite of image processing and exploita...
 
Online feature selection for pixel classification
Found in: Proceedings of the 22nd international conference on Machine learning (ICML '05)
By Damian Eads, James Theiler, Karen Glocer
Issue Date:August 2005
pp. 249-256
Online feature selection (OFS) provides an efficient way to sort through a large space of features, particularly in a scenario where the feature space is large and features take a significant amount of memory to store. Image processing operators, and espec...
     
Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware
Found in: Proceedings of the 2001 ACM/SIGDA ninth international symposium on Field programmable gate arrays (FPGA '01)
By James Theiler, John J. Szymanski, Mike Estlick, Miriam Leeser
Issue Date:February 2001
pp. 103-110
In mapping the k-means algorithm to FPGA hardware, we examined algorithm level transforms that dramatically increased the achievable parallelism. We apply the k-means algorithm to multi-spectral and hyper-spectral images, which have tens to hundreds of cha...
     
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