Multi-sensory and Multi-modal Fusion for Sentient Computing

• Addlesee, M., Curwen, R., Hodges, S., Newman, J., Steggles, P., Ward, A., and Hopper, A. 2001. Implementing a sentient computing system. IEEE Computer, 34(8):50–56.

  Google Scholar 

• Bouguet, J.-Y. Matlab calibration toolbox. http://www.vision.caltech.edu/bouguetj.

• Cattin, P., Zlatnik, D., and Borer, R. 2001. Biometric System using Human Gait. Mechatronics and Machine Vision in Practice, (M2VIP).

• Cerney, M. and Vance, J. 2005. Gesture recognition in virtual environments: A review and framework for future development. Technical report, Human Computer Interaction Center, Iowa State University.

• Choudhury, T., Rehg, J., Pavlovic, V., and Pentland, A. 2002. Boosting and structure learning in dynamic Bayesian networks for audio-visual speaker detection. In Proc. Int. Conference on Pattern Recognition.

• Crowley, J., Coutaz, J., Rey, G., and Reignier, P. 2002. Perceptual components for context aware computing. In Proc. Ubicomp.

• Dey, A. 2001. Understanding and using context. Personal and Ubiquitous Computing, 5(1):4–7.

  Article  Google Scholar 

• De la Torre, F. and Black, M. 2001. Robust principal component analysis for computer vision. In Proc. International Conference on Computer Vision.

• De la Torre, F. and Black, M. 2003. Robust parameterized component analysis: Theory and applications to 2d facial appearance models. Computer Vision and Image Understanding.

• Erickson, T. 2002. Some problems with the notion of context-aware computing. Communications of the ACM, 45(2):102–104.

  Article  Google Scholar 

• Fritsch, J., Kleinehagenbrock, M., Lang, S., Plotz, T., Fink, G., and Sagerer, G. 2003. Multi-modal anchoring for human-robot interaction. Robotics and Autonomous Systems, 43(2).

• Garcia, C. and Tziritas, G. 1999. Face detection using quantized skin color regions merging and wavelet packet analysis. IEEE Transactions on Multimedia, 1(3):264–277, 1999.

  Article  Google Scholar 

• Gavrila, D. 1999. The visual analysis of human movement: A survey. Computer Vision and Image Understanding: CVIU, 73(1):82–98.

  Article  MATH  Google Scholar 

• Genco, A. 2005. Three Step Bluetooth Positioning. In LNCS 3479: Location- and Context-Awareness.

• Hanbury, A. 2003. Circular statistics applied to colour images. 8th Computer Vision Winter Workshop.

• Harle, R. 2004. Maintaining World Models in Context-Aware Environments. PhD thesis, University of Cambridge Engineering Department.

• Harle, R. and Hopper, A. 2005. Deploying and evaluating a location-aware system. In Proc. MobiSys 2005.

• Harter, A. and Hopper, A. 1994. A distributed location system for the active office. IEEE Network, 8(1).

• Hazas M., Scott J., and Krumm, J. 2004. Location-aware computing comes of age. IEEE Computer, pp. 95–97.

• Hopper, A. 2000. Sentient computing—the Royal society clifford paterson lecture. Philosophical Transactions of the Royal Society of London, 358(1773):2349–2358.

  Article  MATH  Google Scholar 

• Ipina, D. and Hopper, A. 2002. TRIP: A low-cost vision-based location system for ubiquitous computing. Personal and Ubiquitous Computing, 6(3):206–219.

  Article  Google Scholar 

• Isard, M. and Blake, A. 1998. Condensation—Conditional density propagation for visual tracking. Int. Journal of Computer Vision, 29(1):5–28.

  Article  Google Scholar 

• Mansley, K., Beresford, A., and Scott, D. 2004. The Carrot Approach: Encouraging use of location systems. In Proceedings of UbiComp. Springer.

• McKenna, S., Raja, Y., and Gong, S. 1998. Object tracking using adaptive color mixture models. In Proc. Asian Conference on Computer Vision, pp. 615–622.

• Nummiaro, K., Koller-Meier, E., and Gool, L.V. 2003. An adaptive color-based particle filter. Image and Vision Computing, 21:99–110.

  Article  Google Scholar 

• Perez, P., Vermaak, J., and Blake, A. 2004. Data fusion for visual tracking with particles. In IEEE Trans. on Pattern Analysis and Machine Intelligence.

• Priyantha, N., Allen, K., Balakrishnan, H., and Teller, S.J. 2001. The cricket compass for context-aware mobile applications. In Mobile Computing and Networking, pp. 1–14.

• Sherrah, J. and Gong, S. 2001. Continuous global evidence-based Bayesian modality fusion for simultaneous tracking of multiple objects. In Proc. International Conference on Computer Vision.

• Sinclair, D. 2000. Smooth region structure: Folds, domes, bowls, ridges, valleys and slopes. In Proc. Conference on Computer Vision and Pattern Recognition, pp. 389–394.

• Skocaj, D. and Leonardis, A. 2002. Robust continuous subspace learning and recognition. In Proc. Int. Electrotechnical and Computer Science Conference.

• Spengler, M. and Schiele, B. 2001. Towards robust multi-cue integration for visual tracking. Lecture Notes in Computer Science, 2095:93–106.

  Article  MATH  Google Scholar 

• Stillman, S. and Essa, I. 2001. Towards reliable multimodal sensing in aware environments. In Proc. Perceptual User Interfaces Workshop, ACM UIST.

• Murphy, A.K., Freeman, W., and Mark, A. 2003. Context-based vision system for place and object recognition. In Proc. International Conference on Computer Vision

• Town, C.P. 2004a. Ontology based Visual Information Processing. PhD thesis, University of Cambridge.

• Town, C.P. 2004b. Ontology-driven Bayesian networks for dynamic scene understanding. In Proc. Int. Workshop on Detection and Recognition of Events in Video (at CVPR04).

• Toyama, K. and Horvitz, E. 2000. Bayesian modality fusion: Probabilistic integration of multiple vision algorithms for head tracking. In Proc. Asian Conference on Computer Vision.

• Turk, M. 2004. Computer vision in the interface. Communications of the ACM, 47(1).

Download references