Visual Attention for Robotic Cognition: A Survey <s> W <s> Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention. <s> BIB001 </s> Visual Attention for Robotic Cognition: A Survey <s> W <s> The responses of neurons in cortical areas V2 and V4 can be significantly modulated by attention to particular locations within an input image. We show that such effects emerge naturally when perception is viewed as a probabilistic inference process governed by Bayesian principles and implemented in hierarchical cortical networks. The proposed model can explain a rich variety of attention-related responses in cortical area V4 including multiplicative modulation of tuning curves, restoration of neural responses in the presence of distracting stimuli, and influence of attention on neighboring unattended locations. Our results suggest a new interpretation of attention as a cortical mechanism for reducing perceptual uncertainty by combining top-down task-relevant information with bottom-up sensory inputs in a probabilistic manner. <s> BIB002
HEN it comes to information management, the underlying idea of the primates attention mechanism is simple, yet extremely robust: focus on the piece of information which is the most relevant to a given context. A custom-built attentional circuit in the primates helps them to execute this attention behavior  . The endeavor of robotics research to design a bioinspired visual attention model for the cognitive robot has strong connectivity with the research in cognitive psychology, computational neuroscience, and computer vision as these are the three disciplines which cultivated the basic research on the artificial modeling of human visual attention. The visual attention models developed for robotic cognition heavily rely on the computational models of visual attention proposed in computer vision and computational neuroscience while the inspiration behind all of these models is rooted in the theories of human visual attention proposed in cognitive psychology and neuroscience. The major motivation for developing computational models of visual attention was two-fold: 1) creating a computational tool to test the validity of the theories/hypothesis of visual attention proposed in psychology and neuroscience; and 2) the potential applications of the principle of focused attention in computer vision, video surveillance, and robotics. Accordingly, we observe the rise of two distinct trends in the research on computational modeling of visual attention. The first one is mostly concerned about simulating the neuronal response of the primates during various attentional activities  - BIB002 . The researchers in computational neuroscience and cognitive psychology are dedicated to develop this type of models. The second kind of computational models are concerned about developing a technical system of visual attention while utilizing the unique properties of the biological attention system BIB001 -  . The researchers in computer vision and robotics are the major developers of the technical models of visual attention. Cognitive robotics, probably, is the most recent user of these models. The goal of the survey presented in this article is to shed light on the research on visual attention modeling as a component of robotic cognition. A brief discussion on the visual attention models proposed in computer vision and computational neuroscience, their limitations in the context of robotic applications, and how these limitations trigger the evolution of the robotic model of visual attention will also be discussed. The rest of the paper is organized as follows. Section II describes a brief history of development of the computational model of visual attention. Section III discusses the characteristics of the computational model of attention that caused the rise of a different group of visual attention models for the robot. Section IV discusses the existing research on the modeling of robotic visual attention. Section V provides an overall discussion on the success, possibilities, and open challenges related to the design of a visual attention model as a component of robotic cognition. Finally, Section VI draws the conclusion on this survey.