Computing visual target distinctness through selective filtering, statistical features, and visual patterns

This paper presents three computational visual distinctness measures, computed from image representational models based on selective filtering, statistical features, and visual patterns, respectively. They are applied to quantify the visual distinctness of targets in complex natural scenes. The measure that applies a simple decision rule to the distances between segregated visual patterns is shown (1) to predict human observer performance in search and detection tasks on complex natural imagery, and (2) to correlate strongly with visual target distinctness estimated by human observers. (C) 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)03101-9].