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Biologically-Inspired Models for Attentive Robot Vision: A Review

Biologically-Inspired Models for Attentive Robot Vision: A Review

Amirhossein Jamalian (Technical University of Chemnitz, Germany) and Fred H. Hamker (Technical University of Chemnitz, Germany)
Copyright: © 2016 |Pages: 30
ISBN13: 9781466687233|ISBN10: 1466687231|EISBN13: 9781466687240
DOI: 10.4018/978-1-4666-8723-3.ch003
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MLA

Jamalian, Amirhossein and Fred H. Hamker. "Biologically-Inspired Models for Attentive Robot Vision: A Review." Innovative Research in Attention Modeling and Computer Vision Applications, edited by Rajarshi Pal, IGI Global, 2016, pp. 69-98. http://doi:10.4018/978-1-4666-8723-3.ch003

APA

Jamalian, A., & Hamker, F. H. (2016). Biologically-Inspired Models for Attentive Robot Vision: A Review. In R. Pal (Ed.), Innovative Research in Attention Modeling and Computer Vision Applications (pp. 69-98). IGI Global. http://doi:10.4018/978-1-4666-8723-3.ch003

Chicago

Jamalian, Amirhossein, and Fred H. Hamker. "Biologically-Inspired Models for Attentive Robot Vision: A Review." In Innovative Research in Attention Modeling and Computer Vision Applications. edited by Pal, Rajarshi, 69-98. Hershey, PA: IGI Global, 2016. http://doi:10.4018/978-1-4666-8723-3.ch003

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Abstract

A rich stream of visual data enters the cameras of a typical artificial vision system (e.g., a robot) and considering the fact that processing this volume of data in real-rime is almost impossible, a clever mechanism is required to reduce the amount of trivial visual data. Visual Attention might be the solution. The idea is to control the information flow and thus to improve vision by focusing the resources merely on some special aspects instead of the whole visual scene. However, does attention only speed-up processing or can the understanding of human visual attention provide additional guidance for robot vision research? In this chapter, first, some basic concepts of the primate visual system and visual attention are introduced. Afterward, a new taxonomy of biologically-inspired models of attention, particularly those that are used in robotics applications (e.g., in object detection and recognition) is given and finally, future research trends in modelling of visual attention and its applications are highlighted.

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