NLOOK: a computational attention model for robot vision (original) (raw)
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Selective Review of Visual Attention Models
Technologies for Machine Learning and Vision Applications
The purpose of this chapter is both to review some of the most representative visual attention models, both theoretical and practical, that have been proposed to date, and to introduce the authors’ attention model, which has been successfully used as part of the control system of a robotic platform. The chapter has three sections: in the first section, an introduction to visual attention is given. In the second section, relevant state of art in visual attention is reviewed. This review is organised in three areas: psychological based models, connectionist models, and features-based models. In the last section, the authors’ attention model is presented.
Visual attention model for manipulating human attention by a robot
2014 IEEE International Conference on Robotics and Automation (ICRA), 2014
For smooth interaction between human and robot, the robot should have an ability to manipulate human attention and behaviors. In this study, we developed a visual attention model for manipulating human attention by a robot. The model consists of two modules, such as the saliency map generation module and manipulation map generation module. The saliency map describes the bottom-up effect of visual stimuli on human attention and the manipulation map describes the top-down effect of face, hands and gaze. In order to evaluate the proposed attention model, we measured human gaze points during watching a magic video, and applied the attention model to the video. Based on the result of this experiment, the proposed attention model can better explain human visual attention than the original saliency map.
Recent years have seen an explosion of research on the computational modeling of human visual attention in task free conditions, i.e., given an image predict where humans are likely to look. This area of research could potentially provide general purpose mechanisms for robots to orient their cameras. One difficulty is that most current models of visual saliency are computationally very expensive and not suited to real time implementations needed for robotic applications.
A Knowledge Driven Computational Visual Attention Model
Computational Visual System face complex processing problems as there is a large amount of information to be processed and it is difficult to achieve higher efficiency in par with human system. In order to reduce the complexity involved in determining the saliency region, decomposition of image into several parts based on specific location is done and decomposed part is passed for higher level computations in determining the saliency region with assigning priority to the specific color in RGB model depending on application. These properties are interpreted from the user using the Natural Language Processing and then interfaced with vision using Language Perceptional Translator (LPT). The model is designed for a robot to search a specific object in a real time environment without compromising the computational speed in determining the Most Salient Region.
Object-based Visual Attention: a Model for a Behaving Robot
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, 2005
One of the first steps of any visual system is that of locating suitable interest points, "salient regions", in the scene, to detect events, and eventually to direct gaze toward these locations. In the last few years, object-based visual attention models have received an increasing interest in the literature, the problem, in this case, being that of creating a model of "objecthood" that eventually guides a saliency mechanism. We propose here an object-based model of visual attention and show its instantiation on a humanoid robot. The robot employs action to learn and define its own concept of objecthood.
Adaptive visual attention model
Proc. of Image and Vision Computing New Zealand, 2007
Visual attention, defined as the ability of a biological or artificial vision system to rapidly detect potentially relevant parts of a visual scene, provides a general purpose solution for low level feature detection in a vision architecture. Well considered for its ...
Visual attention for robotic cognition: a survey
2011
The goal of the cognitive robotics research is to design robots with human-like cognition (albeit reduced complexity) in perception, reasoning, action planning, and decision making. Such a venture of cognitive robotics has developed robots with redundant number of sensors and actuators in order to perceive the world and act up on it in a human-like fashion. A major challenge to deal with these robots is managing the enormous amount of information continuously arriving through multiple sensors. The primates master this information management skill through their custom-built attention mechanism. Mimicking the attention behavior of the primates, therefore, has gained tremendous popularity in robotic research in the recent years (Bar-Cohen et al., Biologically Inspired Intelligent Robots, 2003, and B. Webb et al., Biorobotics, 2003). The difficulties of redundant information management, however, is the most severe in case of visual perception of the robots. Even a moderate size image of the natural scene generally contains enough visual information to easily overload the on-line decision making process of an autonomous robot. Modeling primates-like visual attention mechanism for the robot, therefore, is becoming more popular among the robotic researchers. A visual attention model enables the robot to selectively (and autonomously) choose a "behaviorally relevant" segment of visual information for further processing while relative exclusion of the others. This paper sheds light on the ongoing journey of robotics research to achieve a visual attention model which will serve as a component of cognition of the modern-day robots.
Soft Computing, 2017
Fitting the skills of the natural vision is an appealing perspective for artificial vision systems, especially in robotics applications dealing with visual perception of the complex surrounding environment where robots and humans mutually evolve and/or cooperate, or in a more general way, those prospecting human-robot interaction. Focusing the visual attention dilemma through human eye-fixation paradigm, in this work we propose a model for artificial visual attention combining a statistical foundation of visual saliency and a genetic tuning of the related parameters for robots' visual perception. The computational issue of our model relies on the one hand on center-surround statistical features' calculations with a nonlinear fusion of different resulting maps, and on the other hand on an evolutionary tuning of human's gazing way resulting in emergence of a kind of artificial eye-fixation-based visual attention. Statistical foundation and bottom-up nature of the proposed model provide as well the advantage to make it usable without needing prior Communicated by V. Loia.