  Region-based artificial attention constitutes a framework for bio-inspired
attentional processes on an intermediate abstraction level for the use in
computer vision and mobile robotics. Segmentation algorithms produce regions of
coherently colored pixels. These serve as proto-objects on which the
attentional processes determine image portions of relevance. A single
region---which not necessarily represents a full object---constitutes the focus
of attention. For many post-attentional tasks, however, such as identifying or
tracking objects, single segments are not sufficient. Here, we present a
saliency-guided approach that groups regions that potentially belong to the
same object based on proximity and similarity of motion. We compare our results
to object selection by thresholding saliency maps and a further
attention-guided strategy.
