Development of an Autonomous Visual Perception System for Robots Using Object-Based Visual Attention

Unlike the traditional robotic systems in which the perceptual behaviors are manually designed by programmers for a given task and environment, autonomous perception of the world is one of the challenging issues in the cognitive robotics. It is known that the selective attention mechanism serves to link the processes of perception, action and learning (Grossberg, 2007; Tipper et al., 1998). It endows humanswith the cognitive capability that allows them to learn and think about how to perceive the environment autonomously. This visual attention based autonomous perception mechanism involves two aspects: conscious aspect that directs perception based on the current task and learned knowledge, and unconscious aspect that directs perception in the case of facing an unexpected or unusual situation. The top-down attention mechanism (Wolfe, 1994) is responsible for the conscious aspect whereas the bottom-up attentionmechanism (Treisman & Gelade, 1980) corresponds to the unconscious aspect. This paper therefore discusses about how to build an artificial system of autonomous visual perception. Three fundamental problems are addressed in this paper. The first problem is about pre-attentive segmentation for object-based attention. It is known that attentional selection is either space-based or object-based (Scholl, 2001). The space-based theory holds that attention is allocated to a spatial location (Posner et al., 1980). The object-based theory, however, posits that some pre-attentive processes serve to segment the field into discrete objects, followed by the attention that deals with one object at a time (Duncan, 1984). This paper proposes that object-based attention has the following three advantages in terms of computations: 1) Object-based attention is more robust than space-based attention since the attentional activation at the object level is estimated by accumulating contributions of all components within that object, 2) attending to an exact object can provide more useful information (e.g., shape and size) to produce the appropriate actions than attending to a spatial location, and 3) the discrete objects obtained by pre-attentive segmentation are required in the case that a global feature (e.g., shape) is selected to guide the top-down attention. Thus this paper adopts the object-based visual attention theory (Duncan, 1984; Scholl, 2001). Although a few object-based visual attention models have been proposed, such as (Sun, 2008; Sun & Fisher, 2003), developing a pre-attentive segmentation algorithm is still a challenging issue as it is a unsupervised process. This issue includes three types of challenges: 1) The Development of an Autonomous Visual Perception System for Robots Using Object-Based Visual Attention