Towards a unified visual framework in a binocular active robot vision system |
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| Authors: | Gerardo Aragon-Camarasa Haitham Fattah J. Paul Siebert |
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| Affiliation: | 1. Computer Vision and Graphics Group, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, Glasgow G12 8QQ. Scotland, UK;2. Institute for System Level Integration, The Alba Centre, Alba Campus, EH54 7EG, UK;1. CNRS, LAAS, 7, Avenue du Colonel Roche, F-31400 Toulouse, France;2. Univ de Toulouse, UPS, LAAS, F-31400 Toulouse, France;1. College of Information & Communication Engineering, Harbin Engineering University, Harbin, China;2. Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA;3. Center for Excellence in Signal and Image Processing, Department of Electronics and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK;1. Biomedical Engineering Research Group in the School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa;2. MaPS, University of South Africa, South Africa;1. Department of Psychiatry, University of California San Diego, CA, USA;2. Research Service, VA San Diego Healthcare System, San Diego, CA, USA;3. Statistical Sciences Europe, GlaxoSmithKline Pharmaceuticals, Stevenage SG1 2NY, UK;4. Manchester Pharmacy School, University of Manchester, Manchester M13 9PT, UK |
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| Abstract: | This paper presents the results of an investigation and pilot study into an active binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognising objects in a cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the ‘searchlight metaphor’ of biological systems. We present results of an investigation that yield a maximum vergence error of ～6.5 pixels, while ～85% of known objects were recognised in five different cluttered scenes. Finally a ‘stepping-stone’ visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the field of view resulting from any individual saccade. |
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