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What Can We Learn from Biological Vision Studies for Human Motion Segmentation?

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Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4292))

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Abstract

We review recent biological vision studies that are related to human motion segmentation. Our goal is to develop a practically plausible computational framework that is guided by recent cognitive and psychological studies on the human visual system for the segmentation of human body in a video sequence. Specifically, we discuss the roles and interactions of bottom-up and top-down processes in visual perception processing as well as how to combine them synergistically in one computational model to guide human motion segmentation. We also examine recent research on biological movement perception, such as neural mechanisms and functionalities for biological movement recognition and two major psychological tracking theories. We attempt to develop a comprehensive computational model that involves both bottom-up and top-down processing and is deeply inspired by biological motion perception. According to this model, object segmentation, motion estimation, and action recognition are results of recurrent feedforward (bottom-up) and feedback (top-down) processes. Some open technical questions are also raised and discussed for future research.

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References

  1. Johansson, G.: Visual perception of biological motion and a model for its analysis. Perception & Psychophysics 14, 201–211 (1973)

    Article  Google Scholar 

  2. Troje, N.: Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. Journal of Vision 2, 371–387 (2002)

    Article  Google Scholar 

  3. Thornton, I., Pinto, J., Shiffrar, M.: The visual perception of human locomotion. Cognitive Neuropsychology, 535–552 (1998)

    Google Scholar 

  4. Knoblich, G., Thornton, I.M., Grosjean, M., Shiffrar, M.: The human body. Perception from the inside out. Oxford University Press, New York (2006)

    Google Scholar 

  5. Gepshtein, S., Kubovy, M.: The emergence of visual objects in space-time. Proceedings of the National Academy of Sciences of the United States of America 97, 8186–8191 (2000)

    Article  Google Scholar 

  6. Kubovy, M., Gepshtein, S.: Grouping in space and in space-time: An exercise in phenomenological psychophysics. In: Behrmann, M., Kimchi, R., Olson, C. (eds.) Perceptual Organization in Vision: Behavioral and Neural perspectives, pp. 45–85. Lawrence Erlbaum Association, Mahwah (2003)

    Google Scholar 

  7. Kubovy, M., Gepshtein, S.: Gestalt: from phenomena to laws. In: Perceptual Organization for Artificial Vision Systems, pp. 41–71. Academic Publishers, Boston (2000)

    Google Scholar 

  8. Olivers, C.N., Humphreys, G.: Spatiotemporal segregation in visual search: evidence from parietal lesions. Journal of Experimental Psychology: Human Perception and Performance 30, 667–688 (2004)

    Article  Google Scholar 

  9. Ullman, S.: The Interpretation of Visual Motion. MIT Press, Cambridge (1979)

    Google Scholar 

  10. Marr, D.: Vision: a Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman and Company, New York (1982)

    Google Scholar 

  11. Palmer, S.E., Rock, I.: Rethinking perceptual organization: The role of uniform connectedness. Psychonomic Bulletin and Review 1, 29–55 (1994)

    Article  Google Scholar 

  12. Palmer, S.: Vision Science: Photons to Phenomenology. MIT Press, Bradford Books (1999)

    Google Scholar 

  13. McClelland, J.L.: On the time relations of mental processes: An examination of systems of processes in cascade. Psychological Review 86, 287–330 (1979)

    Article  Google Scholar 

  14. Stillings, N.A., Weisler, S.E., Chase, C.H., Feinstein, M.H., Garfield, J.L., Rissland, E.L.: Cognitive Science: An Introduction. MIT Press, Cambridge (1995)

    Google Scholar 

  15. Palmer, S.E., Brooks, J.L., Nelson, R.: When does grouping happen? Acta Psychologica 114, 311–330 (2003)

    Article  Google Scholar 

  16. Clifford, C.W., Freedman, J., Vaina, L.M.: First- and second-order motion perception in gabor micropattern stimuli: Psychophysical and computational modelling. Cogn. Brain Res. 6, 263–271 (1998)

    Article  Google Scholar 

  17. Braddick, O.J.: A short-range process in apparent motion. Vision Res. 14, 519–527 (1974)

    Article  Google Scholar 

  18. Thornton, I.M., Pinto, J., Shiffrar, M.: The visual perception of human locomotion. Cognitive Neuropsychology 15, 535–552 (1998)

    Article  Google Scholar 

  19. Franconeri, S.L., Halberda, J., Feigenson, L., Alvarez, G.A.: Common fate can define objects in multiple object tracking. Journal of Vision 4, 365a (2004)

    Article  Google Scholar 

  20. Mumford, D.: Neuronal architecture for pattern-theoretic problems. MIT Press, Cambridge (1993)

    Google Scholar 

  21. Bullier, J.: Integrated model of visual processing. Brain Reseach Reviews 36, 96–107 (2001)

    Article  Google Scholar 

  22. Ullman, S.: High-level vision: object recognition and visual cognition. MIT Press, Cambridge (1996)

    MATH  Google Scholar 

  23. Kersten, D., Mamassian, P., Yuille, A.: Object perception as bayesian inference. Annual Review of Psychology 55, 271–304 (2004)

    Article  Google Scholar 

  24. Knill, D.C., Richards, W.: Perception as Bayesian Inference. Cambridge Univ. Press, UK (1996)

    MATH  Google Scholar 

  25. Rao, R.P.N., Olshausen, B., Lewicki, M.: Probabilistic Models of the Brain: Perception and Neural Function. MIT Press, Cambridge (2002)

    Google Scholar 

  26. Lee, T.S., Mumford, D.: Hierarchical bayesian inference in the visual cortex. Journal of Optical Society of America 20, 1434–1448 (2003)

    Article  Google Scholar 

  27. Vecera, S.P., O’Reilly, R.C.: Figure-ground organization and object recognition processes: An interactive account. Journal of Experimental Psychology: Human Perception and Performance 24, 441–462 (1998)

    Article  Google Scholar 

  28. Shi, J., Malik, J.: Motion segmentation and tracking using Normalized cuts. In: Proc. of Int. Conf. on Computer Vision, pp. 1151–1160 (1998)

    Google Scholar 

  29. Fowlkes, C., Belongie, S., Malik, J.: Efficient spatiotemporal grouping using the Nystrom method. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 231–238 (2001)

    Google Scholar 

  30. DeMenthon, D., Megret, R.: Spatio-temporal segmentation of video by hierarchical mean shift analysis. Technical Report: LAMP-TR-090/CAR-TR-978/CS-TR-4388/UMIACS-TR-2002-68 (2002)

    Google Scholar 

  31. Greenspan, H., Goldberger, J., Mayer, A.: A probabilistic framework for spatio-temporal video representation amp indexing. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 461–475. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  32. Megret, R., De Menthon, D.: A survey of spatio-temporal grouping techniques. Technical report, University of Maryland, College Park (2002), http://www.umiacs.umd.edu/lamp/pubs/TechReports/

  33. Moscheni, F., Bhattacharjee, S., Kunt, M.: Spatiotemporal segmentation based on region merging. IEEE Trans. Pattern Anal. Mach. Intell. 20, 897–915 (1998)

    Article  Google Scholar 

  34. Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: Video object segmentation using bayes-based temporal tracking and trajectory-based region merging. IEEE Trans. Circuits and Systems for Video Technology 14, 782–795 (2004)

    Article  Google Scholar 

  35. Gelgon, M., Bouthemy, P.: A region-level motion-based graph representation and labeling for tracking a spatial image partition. Pattern Recognition 33, 725–740 (2000)

    Article  Google Scholar 

  36. Wang, D.: Unsupervised video segmentation based on watersheds and temporal tracking. IEEE Trans. Circuits and Systems for Video Technology, 539–546 (1998)

    Google Scholar 

  37. Porikli, F., Wang, Y.: Automatic video object segmentation using volume growing and hierarchical clustering. Journal on Applied Signal Processin 3, 442–453 (2004)

    Google Scholar 

  38. Tsai, Y., Lai, C., Hung, Y., Shih, Z.: A bayesian approach to video object segmentation. IEEE Trans. Circuits syst. video Technology 15, 175–180 (2005)

    Article  Google Scholar 

  39. Hochstein, S., Ahissar, M.: View from the top: Herarchies and reverse hierarchies in the visual system. Neuron 36, 791–804 (2002)

    Article  Google Scholar 

  40. Borenstein, E., Sharon, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  41. Tu, Z., Chen, X., Yuille, A.L., Zhu, S.C.: Image parsing: Unifying segmentation, detection, and recognition. Int’l Journal of Computer Vision 63, 113–140 (2005)

    Article  Google Scholar 

  42. Ungerleider, L.G., Mishkin, M.: Two cortical visual systems, pp. 549–586. MIT Press, Cambridge (1982)

    Google Scholar 

  43. Felleman, D.J., van Essen, D.C.: Distributed hierarchical processing in primate cerebral cortex. Cerebral cortex 1, 1–47 (1991)

    Article  Google Scholar 

  44. Burr, D., Ross, J.: Vision: The world through picket fences. Current Biology 14, 381–382 (2004)

    Article  Google Scholar 

  45. Giese, M.A., Poggio, T.: Neural Mechanisms for the Recognition of Biological Movement. Nature Neuroscience Review 4, 179–192 (2003)

    Article  Google Scholar 

  46. Oram, M.W., Perrett, D.I.: Integration of form and motion in the anterior part of the superior temporal polysensory area (STPa) of the macaque monkey. Journal of neurophysiology 76, 109–129 (1996)

    Google Scholar 

  47. Sajda, P., Baek, K.: Integration of form and motion within a generative model of visual cortex. Neural Networks 17, 809–821 (2004)

    Article  Google Scholar 

  48. Bullier, J.: Integrated model of visual processing. Brain research review 36, 96–107 (2001)

    Article  Google Scholar 

  49. Kahneman, D., Terisman, A., Gibbs, B.J.: The reviewing of object files: object specific integration of information. Cognitive Psychology 24, 175–219 (1992)

    Article  Google Scholar 

  50. Pylyshyn, Z.W., Storm, R.W.: Tracking multiple independent target: Evidence for a parallel tracking mechanism. Spatial Vision 3, 1–19 (1988)

    Article  Google Scholar 

  51. Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proc. IEEE Int’l Conference on Computer Vision (2003)

    Google Scholar 

  52. Lim, H., Morariu, V., Camps, O.I., Sznaier, M.: Dynamic appearance modeling for human tracking. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  53. Tu, Z., Zhu, S.C.: Image segmentation by data-driven markov chain monte carlo. IEEE Trans. on Pattern Anal. Mach. Intell. 24, 657–673 (2002)

    Article  Google Scholar 

  54. Micilotta, A., Bowden, R.: View-based location and tracking of body parts for visual interaction. In: Proc. of British Machine Vision Conference, pp. 849–858 (2004)

    Google Scholar 

  55. Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Processing 13, 1491–1506 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, 74078, USA

    Cheng Chen & Guoliang Fan

Authors
  1. Cheng Chen
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  2. Guoliang Fan
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, C., Fan, G. (2006). What Can We Learn from Biological Vision Studies for Human Motion Segmentation?. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_79

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  • DOI: https://doi.org/10.1007/11919629_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48626-8

  • Online ISBN: 978-3-540-48627-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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