2013/07/22 by Jan Tünnermann, Tünnermann, Jan, Dieter Enns +3
Computer Science · Engineering · #Advanced Memory and Neural Computing #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.1307.5710
arxiv created 2013/07/22 · openalex publication_date 2013/07/22 · arxiv updated 2013/07/23 · openalex created_date 2022/08/28 · openalex updated_date 2026/07/28
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.