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Algorithms for defining visual regions-of-interest: comparison with eye fixations

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2 Author(s)
C. M. Privitera ; Neurol. & Telerobotics Units, California Univ., Berkeley, CA, USA ; L. W. Stark

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 (algorithmically 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 (human identified ROIs). In the 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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:22 ,  Issue: 9 )