Visual attention based information culling for Distributed Virtual Environments

ABSTRACT

The central goal in Distributed Virtual Environments (DVEs) is to provide an experience of the shared world that is perceptually plausible for the immersed user. This has to be achieved in the face of network issues such as bandwidth limitations and latencies which make total synchronism impossible. Techniques have been devised to help; some optimise network use by limiting what is transmitted, others try to disguise the effects of network 'glitches' at the user's client machine - for example smoothing policies. To date such techniques tend to be system-based rather than user-oriented. Yet there is an active body of psychological research, for example in visual attention, which has successfully been employed by the graphics community to yield better perceived results for a given resource. Immersive DVEs are even more critically dependent upon the users' perceived experience. We may expect therefore that such 'psychologically' oriented approaches have even more to offer here. In this paper a visual attention model which exploits the peculiarities of the human visual system is presented. It is based on previous work, and on a series of carefully designed experiments which are used to guide the implementation of the model and to design architectures for DVEs. The proposed architectures are then tested using different DVE experimental applications, which include a highly populated virtual city. The results demonstrate that the characteristics of the human visual system can be exploited to improve network usage and generate a more perceptually plausible environment in the face of incomplete synchronisation.

REFERENCES

• 1: A. Archambault, C. O'Donnell, and P. Schyns. Blind to object changes: Whenlearning the same object at different levels of categorization modifies its perception. PsychologicalScience, 10(3):249--255, 1999.
• 2: Ashweeni Kumar Beeharee , Steve Pettifer , Adrian West, A Framework for Optimising Network Usage for Plausible Distributed Virtual Environments, Virtual Reality: Cognitive Foundations, Technological Issues & Philosophical Implications, p.141-158, January 2001
• 3: Kirsten Cater , Alan Chalmers , Patrick Ledda, Selective quality rendering by exploiting human inattentional blindness: looking but not seeing, Proceedings of the ACM symposium on Virtual reality software and technology, November 11-13, 2002, Hong Kong, China [doi>10.1145/585740.585744]
• 4: W. Chase and H. Simon. Perception in chess. Cognitive Psychology, 4:55--81, 1973.
• 5: D. C. Dennett. Consciousness Explained. Little Brown and Co, 1992.
• 6: R. Desimone and J. Duncan. Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18:193--222, 1995.
• 7: D. Fernandez-Duque and I. Thornton. Change detection without awareness: do explicit reports underestimate the representation of change in the visual system. Visual Cognition, 7:324--344, 2000.
• 8: J. J. Gibson. The Ecological Approach to Visual Perception. 1979.
• 9: Simon Gibson , Roger J. Hubbold, A Perceptually-Driven Parallel Algorithm for Efficient Radiosity Simulation, IEEE Transactions on Visualization and Computer Graphics, v.6 n.3, p.220-235, July 2000 [doi>10.1109/2945.879784]
• 10: M. Glencross, J. Marsh, J. Cook, S. Daubrenet, S. Pettifer, and R. Hubbold. Distributed interactive virtual prototyping. In SIGRAPH 2002 Sketches and Applications Programme, San Antonio, August 2002.
• 11: C. Greenhalgh. Dynamic, embodied multicast groups in massive-2. Technical Report TR-96-8 1, 1996.
• 12: J. Haber, K. Myszkowski, H. Yamauchi, and H.-P. Seidel. Perceptually guided corrective splatting. Computer Graphics Forum, 20(3), 2001.
• 13: M. Hayhoe, D. Besinger, and D. Ballard. Task constraints in visual working memory. Vision Research, 38:125--137, 1998.
• 14: M. Hayhoe, A. Shrivastava, R. Mruczek, and J. B. Pelz. Visual memory and motor planning in a natural task. Journal of Vision, 3:49--63, 2003.
• 15: J. Henderson and A. Hollingworth. Analytic and Holistic Processes in the Perception of Faces, Objects, and Scenes, chapter Eye movements, visual memory and scene representation. Oxford University Press, in press.
• 16: J. B. Hopfinger, M. Buoncore, and G. Mangun. The neural mechanisms of top-down attentional control. Nature Neuroscience, 3(3):284--291, 2000.
• 17: Mojtaba Hosseini , Steve Pettifer , Nicolas D. Georganas, Visibility-based interest management in collaborative virtual environments, Proceedings of the 4th international conference on Collaborative virtual environments, September 30-October 02, 2002, Bonn, Germany [doi>10.1145/571878.571901]
• 18: S. Ishihara. Ishihara test for color blindness. http://www.toledo-bend.com/colorblind/Ishihara.html.
• 19: L. Itti and C. Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40:1489--1506, 2000.
• 20: L. Itti and C. Koch. Computational modelling of visual attention. Nature Reviews, Neuroscience, 2, March 2001.
• 21: Laurent Itti , Christof Koch , Ernst Niebur, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.20 n.11, p.1254-1259, November 1998 [doi>10.1109/34.730558]
• 22: C. Koch and S. Ullman. Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4:219--227, 1985.
• 23: Piërre van de Laar , Tom Heskes , Stan Gielen, Task-dependent learning of attention, Neural Networks, v.10 n.6, p.981-992, Aug. 1997 [doi>10.1016/S0893-6080(97)00031-2]
• 24: A. Luria. Mind of a Mnemonist. Jonathan Cape, 1969.
• 25: A. Marcel. Conscious and unconscious perception. an approach to the relations between phenomenal experience and perceptual processes. Cognitive Psychology, 15:238--300, 1983.
• 26: Vidhya Navalpakkam , Laurent Itti, A Goal Oriented Attention Guidance Model, Proceedings of the Second International Workshop on Biologically Motivated Computer Vision, p.453-461, November 22-24, 2002
• 27: U. Neisser. Cognition and Reality. Freeman, San Francisco, 1976.
• 28: T. O'Rourke and R. Stevenson. Human visual system based wavelet decomposition for image compression. Visual Communication and Image Representation, 6:109--121, 1995.
• 29: S. Pettifer. An operating envrionment for large scale virtual reality. PhD thesis, The University of Manchester, Apr. 1999.
• 30: Z. Pylyshyn. The role of location indexes in spatial perception: A sketch of the FINST spatial index model. Cognition, 32(1):65--97, 1989.
• 31: R. A. Rensink. Seeing, sensing and scrutinizing. Vision Research, 40:1469--1487, 2000.
• 32: L. Standing. Learning 10,000 pictures. Quarterly Journal of Experimental Psychology, 24:207--222.
• 33: Y. L. H. Yee. Spatiotemporal sensitivity and visual attention for efficient rendering of dynamic environments. Master's thesis, 2000.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
          Subjects: Human factors

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software
          Subjects: Distributed systems

  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.7 Three-Dimensional Graphics and Realism
          Subjects: Virtual reality