Abstract
This chapter describes a Distributed Sensor Network (DSN) that is applied to integrating data coming from multiple sensors in the context of important computer vision tasks such as object recognition and scene understanding.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hall, D. L. and Llinas, J. (1997) An introduction to multisensor data fusion, Proceeding of the IEEE 85, 6–23.
Varshney, P.K. (1997) Multisensor data fusion, Electronic and Communication Engineering Journal, 245–253.
Pearson, J.C., Gelfand, J.J., Sullivan, W.E., Peterson, R.M., and Spence, CD. (1988) Neural network approach to sensor fusion, in Proceedings of SPIE on Sensor Fusion, Vol. 931, C.W. Weaver, Ed., Orlando, FL, 103–108.
Jouseau, E. and Dorizzi, B. (1999) Neural networks and fuzzy data fusion — Application to an on-line and real time vehicle detection system, Pattern Recognition Letters 20, 97–107.
Luo, R.C., Lin, M., and Scherp, R.S. (1988) Dynamic multisensor data fusion system for intelligent robots IEEE Journal of Robotic Automation 3, 386–396.
Regazzoni C.S. and Tesei, A. (1996) Distributed data fusion for real-time crowding estimation Signal Processing 53, 47–63.
Hanson, A.R., Riseman, E.M. and Williams, T.D. (1988) Sensor and information fusion from knowledge-based constraints, in Proceedings of SPIE on Sensor Fusion, Vol. 931, C.W. Weaver, Ed., Orlando, FL, 186–196.
Pau, L. (1989) Knowledge Representation Approaches in Sensor Fusion Automatica 25, 207–214.
Hall, D. (1992) Mathematical Techniques in Multisensor Data Fusion, Artech House, Boston, MA.
Luo, R.C. (1989) Multisensor Integration and Fusion in Intelligent Systems, IEEE Transactions on System, Man, and Cybernetics 19, 901–928.
Sakar, S., Boyer, K.L. (1994) A computational structure for preattentive perceptual organization: graphical enumeration and voting methods, IEEE Trans. on System, Man, Cybernetics 24, 246–266.
Foresti, G.L. and Regazzoni, C.S. (2000) A hierarchical approach to feature extraction and grouping, IEEE Transaction on Image Processing 9, (in press).
Iyengar, S.S., Kashyap, R.L and Madan, R.N. (1991) Distributed Sensor Networks — Introduction to the Special Section, IEEE Transactions on System, Man, and Cybernetics 21, 1027–1031.
Utgoff, P.E., (1988) Perceptron tree: a case study in hybrid concept representation, in Proceeding of the VII National Conference on Artificial Intelligence, 601–605.
Sankar, A. and Mammone, R.J. (1993) Growing and pruning neural tree networks, IEEE Transaction on Computers 42,291–299.
Song, H.H. and Lee, S.W. (1998) A self-organizing neural tree for large set pattern classification, IEEE Transaction on Neural Networks 9, 369–380.
Foresti, G.L. (1999) Outdoor Scene Classification by a Neural Tree Based Approach, Pattern Analysis and Applications 2, 129–142.
Foresti, G.L. and Pieroni, G.G. (1997) Exploiting Neural Trees in Range Image Understanding, Pattern Recognition Letters 19, 869–878.
Ripley B.D. (1996) Pattern Recognition and Neural Networks, Cambridge University Press, UK.
Foresti, G.L. (1998) A Line Segment Based Approach for 3D Motion Estimation and Tracking of Multiple Objects, Internationaljournal of Pattern Recognition and Artificial Intelligence 12, 881–900.
Skiestad, K. and Jain, R. (1989) Illumination independent change detection for real world image sequences, Computer Vision Graphics and Image Processing 46, 387–399.
Foresti, G.L. (1999) Object Recognition and Tracking for Remote Video Surveillance, IEEE Transaction on Circuits and Systems for Video Technology 9, 1045–1062.
Nagel, H.H. (1987) On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results, Artificial Intelligence 33, 299–324.
Dalmia, A.K. and Trivedi, M. (1996) Depth extraction using a single moving camera &3x2014; an integration of depth from motion and depth from stereo, Machine Vision and Applications 9, 43–55.
Rajagopalan, A.N. and Chaudhuri, S. (1994) A variational approach to recovering depth from defocused images, IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 1158–1164.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Foresti, G.L. (2002). Distributed Sensor Networks and Neural Trees for Multisensor Data Fusion in Computer Vision. In: Hyder, A.K., Shahbazian, E., Waltz, E. (eds) Multisensor Fusion. NATO Science Series, vol 70. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0556-2_7
Download citation
DOI: https://doi.org/10.1007/978-94-010-0556-2_7
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-0723-1
Online ISBN: 978-94-010-0556-2
eBook Packages: Springer Book Archive