A Bionic Approach to Dynamic, Multimodal Scene Perception and Interpretation in Buildings
Today, building automation is advancing from simple
monitoring and control tasks of lightning and heating towards more
and more complex applications that require a dynamic perception
and interpretation of different scenes occurring in a building. Current
approaches cannot handle these newly upcoming demands. In this
article, a bionically inspired approach for multimodal, dynamic scene
perception and interpretation is presented, which is based on neuroscientific
and neuro-psychological research findings about the perceptual
system of the human brain. This approach bases on data from diverse
sensory modalities being processed in a so-called neuro-symbolic
network. With its parallel structure and with its basic elements being
information processing and storing units at the same time, a very
efficient method for scene perception is provided overcoming the
problems and bottlenecks of classical dynamic scene interpretation
systems.
[1] E. Barnard, B. Palensky, P. Palensky, and D. Bruckner, "Towards Learning
2.0," Proc. I.T. Revolutions, Dec. 2008.
[2] D. Bruckner. Probabilistic Models in Building Automation: Recognizing
Scenarios with Statistical Methods, PhD thesis at the Vienna University
of Technology, 2007.
[3] T. Bucher, C. Curio, J. Edelbrunner, C. Igel, D. Kastrup, I. Leefken, G.
Lorenz, A. Steinhage, and W. von Seelen, "Image Processing and Behavior
Planning for Intelligent Vehicles," IEEE Trans. Industrial Electronics,
vol. 50, no. 1, pp. 62-75 , Feb 2003, doi:10.1109/TIE.2002.807650.
[4] W. Burgstaller. Interpretation of Situations in Buildings, PhD thesis at the
Vienna University of Technology, 2007.
[5] Gemma Calvert, Charles Spencer, and Barry E. Stein, editors. The
Handbook of Multisensory Processes. MIT Press, 2004.
[6] M.M. Chun and J.M. Wolfe. Blackwells Handbook of Perception, chapter
9, pp. 272-310. Oxford, 2001.
[7] E.B. Goldstein, Wahrnehmungspsychologie, Spektrum Akademischer Verlag,
2002.
[8] W.A. Gruver, "Intelligent Robotics in Manufacturing, Service, and Rehabilitation:
An Overview," IEEE Trans. Industrial Electronics, vol. 41, no.
1, pp. 4-11 , Feb 1994, doi:10.1109/41.281602.
[9] E. Jovanov. "A Model for Consciousness: An Engineering Approach,"
Brain and Consciousness, ECPD Symposium, pp. 291-295, 1997.
[10] A.R. Luria, The Working Brain - An Introduction in Neuropsychology,
Basic Books, 1973.
[11] G.Pratl, Processing and Symbolization of Ambient Sensor Data, PhD
thesis at the Vienna University of Technology, 2006.
[12] S. Segvica and S. Ribaric, " Determining the Absolute Orientation
in a Corridor Using Projective Geometry and Active Vision," IEEE
Trans. Industrial Electronics, vol. 48, no. 3, pp. 696-710 , Jun 2001,
doi:10.1109/41.925597.
[13] M. Solms and O. Turnbull, The Brain and the Inner World - An
Introduction to the Neuroscience of Subjective Experience, Other Press
New York, 2002.
[14] S. Tashiro, T. Murakami, "Step Passage Control of a Power-Assisted
Wheelchair for a Caregiver," IEEE Trans. Industrial Electronics, vol. 55,
no. 4, pp. 1715-1721 , April 2008, doi:10.1109/TIE.2008.917061.
[15] R. Velik, A Bionic Model for Human-like Machine Perception, PhD
thesis at the Vienna University of Technology, 2008.
[16] R. Velik and D. Bruckner, "Neuro-symbolic Networks: Introduction to
a New Information Processing Principle," Proc. Conference of Industrial
Informatics, Jul. 2008.
[17] S. Wermter, "Hybrid Neural Symbolic Integration," Proc. International
Conference on Neural Information Processing Systems (ICONIP -98),
Dec. 1998.
[18] P. Wide, "The Electronic Head: A Virtual Quality Instrument" IEEE
Trans. Industrial Electronics, vol. 48, no. 4, pp. 766-769 , Aug 2001,
doi:10.1109/41.937408.
[19] R. Young, J. Kittler, and J. Matas,"Hypothesis selection for scene interpretation
using grammatical models of scene evolution," Proc. Fourteenth
International Conference on Pattern Recognition, pp. 16-20, Aug. 1998.
[1] E. Barnard, B. Palensky, P. Palensky, and D. Bruckner, "Towards Learning
2.0," Proc. I.T. Revolutions, Dec. 2008.
[2] D. Bruckner. Probabilistic Models in Building Automation: Recognizing
Scenarios with Statistical Methods, PhD thesis at the Vienna University
of Technology, 2007.
[3] T. Bucher, C. Curio, J. Edelbrunner, C. Igel, D. Kastrup, I. Leefken, G.
Lorenz, A. Steinhage, and W. von Seelen, "Image Processing and Behavior
Planning for Intelligent Vehicles," IEEE Trans. Industrial Electronics,
vol. 50, no. 1, pp. 62-75 , Feb 2003, doi:10.1109/TIE.2002.807650.
[4] W. Burgstaller. Interpretation of Situations in Buildings, PhD thesis at the
Vienna University of Technology, 2007.
[5] Gemma Calvert, Charles Spencer, and Barry E. Stein, editors. The
Handbook of Multisensory Processes. MIT Press, 2004.
[6] M.M. Chun and J.M. Wolfe. Blackwells Handbook of Perception, chapter
9, pp. 272-310. Oxford, 2001.
[7] E.B. Goldstein, Wahrnehmungspsychologie, Spektrum Akademischer Verlag,
2002.
[8] W.A. Gruver, "Intelligent Robotics in Manufacturing, Service, and Rehabilitation:
An Overview," IEEE Trans. Industrial Electronics, vol. 41, no.
1, pp. 4-11 , Feb 1994, doi:10.1109/41.281602.
[9] E. Jovanov. "A Model for Consciousness: An Engineering Approach,"
Brain and Consciousness, ECPD Symposium, pp. 291-295, 1997.
[10] A.R. Luria, The Working Brain - An Introduction in Neuropsychology,
Basic Books, 1973.
[11] G.Pratl, Processing and Symbolization of Ambient Sensor Data, PhD
thesis at the Vienna University of Technology, 2006.
[12] S. Segvica and S. Ribaric, " Determining the Absolute Orientation
in a Corridor Using Projective Geometry and Active Vision," IEEE
Trans. Industrial Electronics, vol. 48, no. 3, pp. 696-710 , Jun 2001,
doi:10.1109/41.925597.
[13] M. Solms and O. Turnbull, The Brain and the Inner World - An
Introduction to the Neuroscience of Subjective Experience, Other Press
New York, 2002.
[14] S. Tashiro, T. Murakami, "Step Passage Control of a Power-Assisted
Wheelchair for a Caregiver," IEEE Trans. Industrial Electronics, vol. 55,
no. 4, pp. 1715-1721 , April 2008, doi:10.1109/TIE.2008.917061.
[15] R. Velik, A Bionic Model for Human-like Machine Perception, PhD
thesis at the Vienna University of Technology, 2008.
[16] R. Velik and D. Bruckner, "Neuro-symbolic Networks: Introduction to
a New Information Processing Principle," Proc. Conference of Industrial
Informatics, Jul. 2008.
[17] S. Wermter, "Hybrid Neural Symbolic Integration," Proc. International
Conference on Neural Information Processing Systems (ICONIP -98),
Dec. 1998.
[18] P. Wide, "The Electronic Head: A Virtual Quality Instrument" IEEE
Trans. Industrial Electronics, vol. 48, no. 4, pp. 766-769 , Aug 2001,
doi:10.1109/41.937408.
[19] R. Young, J. Kittler, and J. Matas,"Hypothesis selection for scene interpretation
using grammatical models of scene evolution," Proc. Fourteenth
International Conference on Pattern Recognition, pp. 16-20, Aug. 1998.
@article{"International Journal of Information, Control and Computer Sciences:62377", author = "Rosemarie Velik and Dietmar Bruckner", title = "A Bionic Approach to Dynamic, Multimodal Scene Perception and Interpretation in Buildings", abstract = "Today, building automation is advancing from simple
monitoring and control tasks of lightning and heating towards more
and more complex applications that require a dynamic perception
and interpretation of different scenes occurring in a building. Current
approaches cannot handle these newly upcoming demands. In this
article, a bionically inspired approach for multimodal, dynamic scene
perception and interpretation is presented, which is based on neuroscientific
and neuro-psychological research findings about the perceptual
system of the human brain. This approach bases on data from diverse
sensory modalities being processed in a so-called neuro-symbolic
network. With its parallel structure and with its basic elements being
information processing and storing units at the same time, a very
efficient method for scene perception is provided overcoming the
problems and bottlenecks of classical dynamic scene interpretation
systems.", keywords = "building automation, biomimetrics, dynamic scene interpretation,human-like perception, neuro-symbolic networks.", volume = "3", number = "4", pages = "1170-9", }