13 May 2010 Human-guided visualization enhances automated target detection
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Abstract
Automated target cueing (ATC) can assist analysts with searching large volumes of imagery. Performance of most automated systems is less than perfect, requiring an analyst to review the results to dismiss false alarms or confirm correct detections. This paper explores methods for improving the presentation and visualization of the ATC output, enabling more efficient and effective review of the detections flagged by the ATC. The techniques presented in this paper are applicable to a wide range of search problems using data from different sensors modalities. The information available to the computer increases as ATC detections are either accepted or rejected by the analyst. It is often easy to confirm obviously correct detections and dismiss obvious false alarms, which provides the starting point for the automated updating of the visualization. In machine learning algorithms, this information can be used to retrain or refine the classifier. However, this retraining process is appropriate only when future sensor data is expected to closely resemble the current set. For many applications, the sensor data characteristics (viewing geometry, resolution, clutter complexity, prevalence and types of confusers) are likely to change from one data collection to the next. For this reason, updating the visualization for the current data set, rather than updating the classifier for future processing, may prove more effective. This paper presents an adaptive visualization technique and illustrates the technique with applications.
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John M. Irvine, John M. Irvine, } "Human-guided visualization enhances automated target detection", Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960U (13 May 2010); doi: 10.1117/12.850583; https://doi.org/10.1117/12.850583
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