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Region-based artificial attention constitutes a framework for bio-inspiredattentional processes on an intermediate abstraction level for the use incomputer vision and mobile robotics. Segmentation algorithms produce regions ofcoherently colored pixels. These serve as proto-objects on which theattentional processes determine image portions of relevance. A singleregion---which not necessarily represents a full object---constitutes the focusof attention. For many post-attentional tasks, however, such as identifying ortracking objects, single segments are not sufficient. Here, we present asaliency-guided approach that groups regions that potentially belong to thesame object based on proximity and similarity of motion. We compare our resultsto object selection by thresholding saliency maps and a furtherattention-guided strategy.

Saliency-Guided Perceptual Grouping Using Motion Cues in Region-Based Artificial Visual Attention