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A stochastic model for the detection of coherent motion

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Abstract

A computational model is presented for the detection of coherent motion based on template matching and hidden Markov models. The premise of this approach is that the growth in detection sensitivity is greater for coherent motion of structured forms than for random coherent motion. In this preliminary study, a recent experiment was simulated with the model and the results are shown to be in agreement with the above premise. This model can be extended to be part of a more complex and elaborate computational visual system.

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References

  1. Adelson EA, Bergen JR (1985) Spatio-temporal energy models for the preception of motion. J Opt Soc Am A 2:284–299

    Google Scholar 

  2. Barlow HB, Tripathy SP (1997) Correspondence noise and signal pooling in the detection of coherent motion. J Neurosci 17:7954–7966

    Google Scholar 

  3. Braddick OB, O’Brien JM, Wattam-Bell J, Atkinson J, Hartley T, Turner R (2000) Brain areas sensitive to coherent visual motion. Perception 30:61–72

    Google Scholar 

  4. Bregler C (1997) Learning and recognizing human dynamics in video sequences. In: Proceedings of IEEE conference on computer vision and pattern recognition, San Juan, Puerto Rico, 17–19 June 1981, pp 568–574

  5. Britten KH, Shadlen MN, Newsome WT, Movshon JA (1992) The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12:4745–4765

    Google Scholar 

  6. Brownlow S, Dixon AR, Egbert CA, Radcliffe RD (1997) Perception of movement and dancer characteristics from point-light displays of dance. Psychol Rec 47:411–421

    Google Scholar 

  7. Celebrini S, Newsome WT (1994) Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the Macaque monkey. J Neurosci 14:4109–4124

    Google Scholar 

  8. Cutting JE (1978) A program to generate synthetic walkers as dynamic point-light displays. Behav Res Meth Instrum 10:91–94

    Google Scholar 

  9. Cutting JE, Proffitt DR (1981) Gait perception as an example of how we may perceive events. In: Walk R, Pick HL (eds) Intersensory perception and sensory integration. Plenum, New York, pp 249–273

  10. Dawson MR (1991) The how and why of what went where in apparent motion: modeling solutions to the motion correspondence problem. Psychol Rev 98:569–603

    Google Scholar 

  11. Desimone R, Albright TD, Gross CG, Bruce C (1984) Stimulus-selective properties of inferior temporal neurons in the macaque. J Neurosci 4:2051–2062

    Google Scholar 

  12. Dittrich WH (1993) Action categories and the perception of biological motion. Perception 22:15–22

    Google Scholar 

  13. Downing PE, Jiang Y, Shuman M, Kanwisher N (2001) A cortical area selective for visual processing of the human body. Science 293:2470–2473

    Google Scholar 

  14. Fredericksen RE, Verstraten FA, van de Grind WA (1993) Spatio-temporal characteristics of human motion perception. Vis Res 33:1193–1205

    Google Scholar 

  15. Giese MA, Poggio T (2003) Nerual mechanisms for the recognition of biological movements. Nat Rev Neurosci 4:179–192

    Google Scholar 

  16. Goddard N (1992) The perception of articulated motion: recognizing moving light displays. PhD Thesis, University of Rochester, Rochester, NY

  17. Gold B, Morgan N (1999) Speech and audio signal processing: processing and perception of speech and music. Wiley, New York

  18. Green DM, Swets JA (1966) Signal detection theory and psychophysics. Krieger, New York

  19. Gross CG, Rocha-Miranda CE, Bender DB (1972) Visual properties of neurons in inferotemporal cortex of the macque. J Neurophysiol 35:96–111

    Google Scholar 

  20. Grossman ED, Blake R (2002) Brain areas active during visual perception of biological motion. Neuron 35:1167–1175

    Google Scholar 

  21. Grossman ED, Donnelly M, Prices R, Pickens D, Morgan V, Neighbor G, Blake R (2000) Brain areas involved in perception of biological motion.J Cogn Neurosci 12:711–720

    Google Scholar 

  22. Grzywacz NM, Watamaniuk SNJ, McKee SP (1995) Temporal coherence theory for the detection and measurement of visual motion. Vis Res 35:3183–3203

    Google Scholar 

  23. Johansson G (1973) Visual perception of biological motion and a model for its analysis. Percept Psychophys 14:201–211

    Google Scholar 

  24. Kozlowski LT, Cutting JE (1977) Recognizing the sex of a walker from a dynamic point-light display. Percept Psychophys 21:575–580

    Google Scholar 

  25. Lee J (2003) A computational model for biological motion perception. Master’s Thesis, University of Toronto

  26. Marr D (1982) Vision. Freeman, San Francisco

  27. Mather G, Murdoch L (1994) Gender discrimination in biological motion displays based on dynamic cues. Proc R Soc Lond B 258:273–279

    Google Scholar 

  28. Murphy K (2003) Hidden Markov Model (HMM) Toolbox. http://www.ai.mit.edu/ murphyk/Software/HMM/hmm.html

  29. Neri P, Morrone MC, Burr DC (1998) Seeing biological motion. Nature 395:894–896

    Google Scholar 

  30. Newsome WT, Britten KH, Movshon JA (1989) Neuronal correlates of a perceptual decision. Nature 341:52–54

    Google Scholar 

  31. Peterson MA, Gibson BS (1991) The initial identification of figure-ground relationships: contributions from shape recognition processes. Bull Psychon Soc 29:199–202

    Google Scholar 

  32. Poritz AB (1988) Hidden Markov models: a guided tour. In: Proceedings of the IEEE conference on acoustics, speech and signal processing1. IEEE Press, New York, 1:7–13

  33. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77:257–286

    Google Scholar 

  34. Tripathy SP, Mussap AJ, Barlow HB (1999) Detecting collinear dots in noise. Vis Res 39:4161–4171

    Google Scholar 

  35. Troje NF (2002) Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. J Vis 2:371–387

    Google Scholar 

  36. Vaina LM, Solomon J, Chowdhury S, Sinha P, Belliveau JW (2001) Functional neuroanatomy of biological motion perception in humans. Proc Nat Acad Sci USA 98:11656–11661

    Google Scholar 

  37. Watamaniuk SN, McKee SP, Grywacz NM (1995) Detecting a trajectory embedded in random-direction motion noise. Vis Res 35:65–77

    Google Scholar 

  38. Wong W, Barlow HB (2000) Tunes and templates. Nature 404:952–953

    Google Scholar 

  39. Yamato J, Ohya J, Ishii K (1992) Recognizing human action in time-sequential images using hidden Markov model. In: Proceedings of IEEE conference on computer vision and pattern recognition, Champaign, IL, 15–18 June 1992, pp 379–385

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Correspondence to Willy Wong.

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Lee, J., Wong, W. A stochastic model for the detection of coherent motion. Biol. Cybern. 91, 306–314 (2004). https://doi.org/10.1007/s00422-004-0516-0

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  • DOI: https://doi.org/10.1007/s00422-004-0516-0

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