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normalizing images so that trivial changes in lighting or technical factors will not be called a change, and he introduces advanced concepts from statistical modeling and hypothesis testing; yet he stops short of application-specific “change understanding,” his term for classifying changes as meaningful to the end user.

There is a strong need for developing rigorous methods not only for detecting changes between images but also for using them to extract meaningful information about the objects being imaged. One approach is the use of statistical decision theory, where the statistical properties of images of normally evolving spatiotemporal objects are modeled, and “meaningful” is defined in terms of deviations from these normal models. Alternatively, specific statistical models can also be devised for various classes of interesting changes, and in this case “meaningful” can be defined in terms of classification accuracy or costs assigned to misclassification.

A different approach is to recognize key components of the evolving images and their spatiotemporal relation to one another. This semantic approach is similar in spirit to what the human visual and cognitive system does in analyzing scenes containing well-delineated, temporally varying object components, but computer implementations can take into account the noise and resolution characteristics of the images.

For statistical or semantic approaches, or any synthesis of the two, there is a pressing need for assessing the efficacy of the change detection and analysis methods in terms of the specific task for which the images were produced. This assessment could then be used to optimize both the algorithms themselves and the imaging systems that acquire the spatiotemporal data.

KEY QUESTIONS

  • What fields of application, within the expertise of the participants, require careful discrimination between meaningful and trivial changes? In each, what are the characteristics of meaningful change?

  • In each field identified, what databases of imagery or other data can be used to build models of meaningful changes?

  • Can fully autonomous computer algorithms compete with a human analyst looking for meaningful changes? How can the computer enhance the capabilities of the expert human? By analogy to computer-aided detection (CAD) or diagnosis (CADx) in medicine, can computer-aided change detection (CACD) be applied in the applications identified?



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