Visual attention is one of the important phenomena in biological vision which can be followed to achieve more efficiency, intelligence, and robustness in artificial vision systems. This paper investigates a region-based approach that performs pixel clustering prior to the processes of attention in contrast to late clustering as done by contemporary methods. The foundation steps of feature map construction for the region-based attention model are proposed here. The color contrast map is generated based upon the extended findings from the color theory, the symmetry map is constructed using a novel scanning-based method, and a new algorithm is proposed to compute a size contrast map as a formal feature channel. Eccentricity and orientation are computed using the moments of obtained regions and then saliency is evaluated using the rarity criteria. The efficient design of the proposed algorithms allows incorporating five feature channels while maintaining a processing rate of multiple frames per second. Another salient advantage over the existing techniques is the reusability of the salient regions in the high-level machine vision procedures due to preservation of their shapes and precise locations. The results indicate that the proposed model has the potential to efficiently integrate the phenomenon of attention into the main stream of machine vision and systems with restricted computing resources such as mobile robots can benefit from its advantages.

This paper presents a new approach for providing visual attention on robot vision systems. Compared to other approaches our method is very fast as it processes regions rather than individual pixels. The proposed method first builds a list of regions by applying a shade and shadow tolerant segmentation step. The features of these regions are computed using their convex hulls in order to simplify and accelerate the processing. Feature values are stored within the records of respective regions instead of constructing a master map of attention. Then an algorithmic method is applied for finding the focus of attention in contrast to mathematical approaches used by existing models. Experiments conducted on simulated and real image data have not only demonstrated the validity of the proposed approach but have also led to the establishment of a comprehensive robotic vision system.