Abstract
We present a new method for characterizing information about a target relative to its background. The resultant computational measures are then applied to quantify the visual distinctness of targets in complex natural backgrounds from digital imagery. A generalization of the Kullback-Leibler joint information gain over the optimal interest points of the target image is shown to correlate strongly with visual target dis- tinctness as estimated by human observers. Optimal interest points are defined as spatial locations of partially invariant features, which minimize the error probability between the target and the nontarget scenes; their significance is a function of the corresponding degree of congruence across scales and orientations. © 2001 Society of Photo-Optical Instrumentation Engineers. (DOI: 10.1117/1.1389064)
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