Fast and robust generation of feature maps for region-based visual attention.
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.
Aziz
Muhammad Zaheer
MZ
GET Lab, Paderborn University, 33098 Paderborn, Germany. aziz@upb.de
Mertsching
Bärbel
B
D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention; D032701:Biomimetics / Q000379:methods*; D016000:Cluster Analysis; D003116:Color; D003124:Colorimetry / Q000379:methods; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D010364:Pattern Recognition, Visual; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity