Abstract
Nowadays, the computer vision community conducts an effort to produce canny systems able to tackle unconstrained environments. However, the information contained in images is so massive that fast and reliable knowledge extraction is impossible without restricting the range of expected meaningful signals. Inserting a priori knowledge on the operative “context” and adding expectations on object appearances are recognized today as a feasible solution to the problem. This paper attempts to define “context” in robotic vision by introducing a summarizing formalization of previous contributions by multiple authors. Starting from this formalization, we analyze one possible solution to introduce context-dependency in vision: an opportunistic switching strategy that selects the best fitted scenario among a set of pre-compiled configurations. We provide a theoretical framework for “context switching” named Context Commutation, grounded on Bayesian theory. Finally, we describe a sample application of the above ideas to improve video surveillance systems based on background subtraction methods.
This is a preview of subscription content, log in via an institution to check access.
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
Coutelle, C., 1995. Conception d’un système à base d’opérateurs de vision rapides, PhD thesis (in French), Université de Paris Sud (Paris 11), Paris, France.
Crowley, J.L., J. Coutaz, G. Rey, P. Reignier, 2002. Perceptual Components for Context Aware Computing. In Proc. UBICOMP2002, Sweden, available at http://citeseer.nj.nec.com/541415.html.
Dessoude, O., 1993. Contrôle Perceptif en milieu hostile: allocation de ressources automatique pour un système multicapteur, PhD thesis (in French), Université de Paris Sud (Paris 11), Paris, France.
Draper, B.A., J.Bins, K.Baek, 1999. ADORE: Adaptive Object Recognition. In Proc. ICVS99, pp. 522–537.
Dubes, R. C., Jain, A. K., 1989. Random Field Models in Image Analysis. In J. Applied Statistics, V. 16, pp.131–164.
Ebner, M., A. Zell, 1999. Evolving a task specific image operator. In Proc. 1 st European Wshops on Evolutionary Image Analysis, Signal Processing and Telecommunications, Göteborg, Sweden, Springer-Verlag, pp. 74–89.
Horswill, I., 1995. Analysis of Adaptation and Environment. In Artificial Intelligence, V. 73(1–2), pp. 1–30, 1995.
Kittler, J., J. Matas, M. Bober, L. Nguyen, 1995. Image interpretation: Exploiting multiple cues. In Proc. Int. Conf. Image Processing and Applications, Edinburgh, UK, pp. 1–5.
Kruppa, H., M. Spengler, B. Schiele, 2001. Context-driven Model Switching for Visual Tracking. In Proc. 9 th Int. Symp. Intell. Robotics Sys., Toulouse, France.
Lombardi, P., 2003. A Model of Adaptive Vision System: Application to Pedestrian Detection by Autonomous Vehicles. PhD thesis (in English), Università di Pavia (Italy) and Université de Paris XI (France).
Merlo, X., 1988. Techniques probabilistes d’intégration et de contrôle de la perception en vue de son exploitation par le système de décision d’un robot, PhD thesis (in French), Université de Paris Sud (Paris 11), Paris, France.
Rabiner, L.R., 1989. A tutorial on hidden Markov models. InProceedings of the IEEE, V. 77, pp. 257–286.
Rimey, R.D., 1993. Control of Selective Perception using Bayes Nets and Decision Theory. Available at http://citeseer.nj.nec.com/rimey93control.html.
Roli, F., G. Giacinto, S.B. Serpico, 2001. Classifier Fusion for Multisensor Image Recognition. In Image and Signal Processing for Remote Sensing VI, Sebastiano B. Serpico, Editor, Proceedings of SPIE, V. 4170, pp.103–110.
Rosenfeld, A., R.A. Hummel, S.W. Zucker, 1976. Scene labeling by relaxation operations. In IEEE Trans. Syst. Man Cybern., V. 6, pp. 420–433.
Shekhar, C., S. Kuttikkad, R. Chellappa, 1996. KnowledgeBased Integration of IU Algorithms. In Proc. Image Understanding Workshop, ARPA, V. 2, pp. 1525–1532, 1996.
Stauffer, C., W.E.L. Grimson, 1999. Adaptive Background Mixture Models for Real-Time Tracking. In Proc. IEEE Conf. Comp. Vis. Patt. Rec. CVPR99, pp. 246–252.
Strat, T.M., 1993. Employing Contextual Information in Computer Vision. In Proc. DARPA93, pp. 217–229.
Tissainayagam, P., D. Suter, 2003. Contour tracking with automatic motion model switching. In Pattern Recognition.
Torralba, A., K.P. Murphy, W.T. Freeman, M.A. Rubin, 2003. Context-based vision system for place and object recognition. In Proc. ICCV’03, available at http://citeseer.nj.nec.com/torralba03contextbased.html.
Toyama, K., E.Horvitz, 2000. Bayesian Modality Fusion: Probabilistic Integration of Multiple Vision Algorithms for Head Tracking. In Proc. ACCV’00, 4 th Asian Conf. Comp. Vision, Tapei, Taiwan, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Lombardi, P., Cantoni, V., Zavidovique, B. (2006). CONTEXT IN ROBOTIC VISION Control for real-time adaptation. In: BRAZ, J., ARAÚJO, H., VIEIRA, A., ENCARNAÇÃO, B. (eds) INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS I. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4543-3_29
Download citation
DOI: https://doi.org/10.1007/1-4020-4543-3_29
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-4136-5
Online ISBN: 978-1-4020-4543-1
eBook Packages: Computer ScienceComputer Science (R0)