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<p><b>Abstract</b>—Many machine vision applications, such as compression, pictorial database querying, and image understanding, often need to analyze in detail only a representative subset of the image, which may be arranged into sequences of loci called regions-of-interest, ROIs. We have investigated and developed a methodology that serves to automatically identify such a subset of aROIs (<it>a</it>lgorithmically detected ROIs) using different Image Processing Algorithms, IPAs, and appropriate clustering procedures. In human perception, an internal representation directs top-down, context-dependent sequences of eye movements to fixate on similar sequences of hROIs (<it>h</it>uman identified ROIs). In this paper, we introduce our methodology and we compare aROIs with hROIs as a criterion for evaluating and selecting bottom-up, context-free algorithms. An application is finally discussed.</p>
Eye movements, scanpath theory, regions of interest identification and comparison.

C. M. Privitera and L. W. Stark, "Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 22, no. , pp. 970-982, 2000.
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