A new technique is described for the registration of edge-detected images. While an extensive literature exists on the problem of image registration, few of the current approaches include a well-defined measure of the statistical confidence associated with the solution. Such a measure is essential for many autonomous applications, where registration solutions that are dubious (involving poorly focused images or terrain that is obscured by clouds) must be distinguished from those that are reliable (based on clear images of highly structured scenes). The technique developed herein utilizes straightforward edge pixel matching to determine the "best" among a class of candidate translations. A well-established statistical procedure, the McNemar test, is then applied to identify which other candidate solutions are not significantly worse than the best solution. This allows for the construction of confidence regions in the space of the registration parameters. The approach is validated through a simulation study and examples are provided of its application in numerous challenging scenarios. While the algorithm is limited to solving for two-dimensional translations, its use in validating solutions to higher-order (rigid body, affine) transformation problems is demonstrated.

In this paper, we derive a new optimal change metric to be used in synthetic aperture RADAR (SAR) coherent change detection (CCD). Previous CCD methods tend to produce false alarm states (showing change when there is none) in areas of the image that have a low clutter-to-noise power ratio (CNR). The new estimator does not suffer from this shortcoming. It is a surprisingly simple expression, easy to implement, and is optimal in the maximum-likelihood (ML) sense. The estimator produces very impressive results on the CCD collects that we have tested.

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A new method is introduced for real-time detection of transient change in scenes observed by staring sensors that are subject to platform jitter, pixel defects, variable focus, and other real-world challenges. The approach uses flexible statistical models for the scene background and its variability, which are continually updated to track gradual drift in the sensor's performance and the scene under observation. Two separate models represent temporal and spatial variations in pixel intensity. For the temporal model, each new frame is projected into a low-dimensional subspace designed to capture the behavior of the frame data over a recent observation window. Per-pixel temporal standard deviation estimates are based on projection residuals. The second approach employs a simple representation of jitter to generate pixelwise moment estimates from a single frame. These estimates rely on spatial characteristics of the scene, and are used gauge each pixel's susceptibility to jitter. The temporal model handles pixels that are naturally variable due to sensor noise or moving scene elements, along with jitter displacements comparable to those observed in the recent past. The spatial model captures jitter-induced changes that may not have been seen previously. Change is declared in pixels whose current values are inconsistent with both models.

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In November of 2000, the Deputy Under Secretary of Defense for Science and Technology Sensor Systems (DUSD (S&T/SS)) chartered the Automatic Target Recognizer Working Group (ATRWG) to develop guidelines for sanctioned Problem Sets. Such Problem Sets are intended for development and test of Automatic Target Recognition (ATR) algorithms and contain comprehensive documentation of the data in them. A Problem Set provides a consistent basis for examining ATR performance and growth. Problem Sets will, in general, serve multiple purposes. First, they will enable informed decisions by government agencies sponsoring ATR development and transition. Problem Sets standardize the testing and evaluation process, offering a consistent assessment of ATR performance. Second, they will measure and guide ATR development progress within this standardized framework. Finally, they quantify the state-of-the-art for the community. Problem Sets provide clearly defined operating condition coverage. This encourages ATR developers to consider these critical challenges and allows evaluators to assess over them. The widely distributed development and self-test portions, along with a documented disciplined methodology, permit ATR developers to address critical issues and describe their accomplishments, while the sequestered portion permits government assessment of state-of-the-art and of transition readiness. This paper discusses the elements of an ATR Problem Set as a package of data and information that presents a standardized ATR challenge relevant to one or more scenarios. The package includes training and test data containing targets and clutter, truth information, required experiments, and a standardized analytical methodology to assess performance. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/16/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Proc. of SPIE Vol. 4726 313 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/16/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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