Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 723213, 12 pages
http://dx.doi.org/10.1155/2014/723213
Research Article

Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia

Received 27 May 2014; Accepted 26 June 2014; Published 3 September 2014

Academic Editor: Shifei Ding

Copyright © 2014 Bardia Yousefi and Chu Kiong Loo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. H. Adelson and J. R. Bergen, “Spa-tiotemporal energy models for the perception of motion,” Journal of the Optical Society of America A: Optics and Image Science, and Vision, vol. 2, no. 2, pp. 284–299, 1985. View at Google Scholar · View at Scopus
  2. S. Shioiri and P. Cavanagh, “ISI produces reverse apparent motion,” Vision Research, vol. 30, no. 5, pp. 757–768, 1990. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Shioiri and K. Matsumiya, “Motion mechanisms with different spa- tiotemporal characteristics identied by an MAE technique with superimposed gratings,” Journal of Vision, vol. 9, no. 5, article 30, 2009. View at Google Scholar
  4. K. Moutoussis and S. Zeki, “A direct demonstration of perceptual asynchrony in vision,” Proceedings of the Royal Society B: Biological Sciences, vol. 264, no. 1380, pp. 393–399, 1997. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Whitney and I. Murakami, “Latency difference, not spatial extrapolation,” Nature Neuroscience, vol. 1, pp. 656–657, 1998. View at Publisher · View at Google Scholar · View at Scopus
  6. A. O. Holcombe, “Seeing slow and seeing fast: two limits on perception,” Trends in Cognitive Sciences, vol. 13, no. 5, pp. 216–221, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Thorpe, D. Fize, and C. Marlot, “Speed of processing in the human visual system,” Nature, vol. 381, no. 6582, pp. 520–522, 1996. View at Publisher · View at Google Scholar · View at Scopus
  8. M. A. Giese and T. Poggio, “Neural mechanisms for the recognition of biological movements,” Nature Reviews Neuroscience, vol. 4, no. 3, pp. 179–192, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Riesenhuber and T. Poggio, “Hierarchical models of object recognition in cortex,” Nature Neuroscience, vol. 2, no. 11, pp. 1019–1025, 1999. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Riesenhuber and T. Poggio, “Neural mechanisms of object recognition,” Current Opinion in Neurobiology, vol. 12, no. 2, pp. 162–168, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Schindler and L. Van Gool, “Action Snippets: how many frames does human action recognition require?” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), vol. 1–12, pp. 3025–3032, Anchorage, Alaska, USA, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Danafar, A. Giusti, and J. Schmidhuber, “Novel kernel-based recognizers of human actions,” EURASIP Journal on Advances in Signal Processing, vol. 2010, Article ID 202768, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Danafar, A. Gretton, and J. Schmidhuber, “Characteristic kernels on structured domains excel in robotics and human action recognition,” in Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, pp. 264–279, Springer, 2010. View at Google Scholar
  14. L. L. Cloutman, “Interaction between dorsal and ventral processing streams: where, when and how?” Brain and Language, vol. 127, no. 2, pp. 251–263, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Mather, A. Pavan, R. Bellacosa Marotti, G. Campana, and C. Casco, “Interactions between motion and form processing in the human visual system,” Frontiers in Computational Neuroscience, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. B. M. Dow, A. Z. Snyder, R. G. Vautin, and R. Bauer, “Magnification factor and receptive field size in foveal striate cortex of the monkey,” Experimental Brain Research, vol. 44, no. 2, pp. 213–228, 1981. View at Google Scholar · View at Scopus
  17. S. Eifuku and R. H. Wurtz, “Response to motion in extrastriate area MSTI: center-surround interactions,” Journal of Neurophysiology, vol. 80, no. 1, pp. 282–296, 1998. View at Google Scholar · View at Scopus
  18. J. P. Jones and L. A. Palmer, “An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex,” Journal of Neurophysiology, vol. 58, no. 6, pp. 1233–1258, 1987. View at Google Scholar · View at Scopus
  19. Z. Kourtzi and N. Kanwisher, “Activation in human MT/MST by static images with implied motion,” Journal of Cognitive Neuroscience, vol. 12, no. 1, pp. 48–55, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. K. S. Saleem, W. Suzuki, K. Tanaka, and T. Hashikawa, “Connections between anterior inferotemporal cortex and superior temporal sulcus regions in the macaque monkey,” Journal of Neuroscience, vol. 20, no. 13, pp. 5083–5101, 2000. View at Google Scholar · View at Scopus
  21. M. A. Giese and L. M. Vaina, “Pathways in the analysis of biological motion: computational model and fMRI results,” Perception, vol. 30, p. 119, 2001. View at Google Scholar
  22. R. Laycock, S. G. Crewther, and D. P. Crewther, “A role for the “magnocellular advantage” in visual impairments in neurodevelopmental and psychiatric disorders,” Neuroscience and Biobehavioral Reviews, vol. 31, no. 3, pp. 363–376, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Wang, Y. Liu, W. Wang, W. Xu, and M. Zhang, “Multi-scale locality-constrained spatiotemporal coding for local feature based human action recognition,” The Scientific World Journal, vol. 2013, Article ID 405645, 11 pages, 2013. View at Publisher · View at Google Scholar
  24. Y. N. Wu, Z. Si, H. Gong, and S. Zhu, “Learning active basis model for object detection and recognition,” International Journal of Computer Vision, vol. 90, no. 2, pp. 198–235, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature, vol. 381, no. 6583, pp. 607–609, 1996. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Liu, Beyond pixels: exploring new representations and applications for motion analysis [Ph.D. thesis], Massachusetts Institute of Technology, 2009.
  27. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, “High accuracy optical flow estimation based on a theory for warping,” in Computer Vision—ECCV 2004, vol. 3024 of Lecture Notes in Computer Science, pp. 25–36, Springer, Berlin, Germany, 2004. View at Publisher · View at Google Scholar
  28. A. Bruhn, J. Weickert, and C. Schnörr, “Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods,” International Journal of Computer Vision, vol. 61, no. 3, pp. 211–231, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. L. Alvarez, R. Deriche, T. Papadopoulo, and J. Sanchez, “Symmetrical dense optical flow estimation with occlusions detection,” in Proceedings of the Computer Vision (Eccv '02), vol. 2350, pp. 721–735, 2002.
  30. L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, pp. 338–353, 1965. View at Google Scholar · View at MathSciNet · View at Scopus
  31. B. Chen and X. Liu, “Delay-dependent robust H control for T-S fuzzy systems with time delay,” IEEE Transactions on Fuzzy Systems, vol. 13, no. 4, pp. 544–556, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. B. Yousefi and C. K. Loo, “Development of biological movement recognition by interaction between active basis model and fuzzy optical flow division,” The Scientific World Journal, vol. 2014, Article ID 238234, 14 pages, 2014. View at Publisher · View at Google Scholar
  33. G. Huang, Q. Zhu, and C. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 985–990, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  34. D. Wang and G.-B. Huang, “Protein sequence classification using extreme learning machine,” in Proceeding of the International Joint Conference on Neural Networks (IJCNN ’05), vol. 3, pp. 1406–1411, can, July 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. G. Huang, Q. Zhu, and C. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. R. Rajesh and J. S. Prakash, “Extreme learning machines—a review and state-of-the-art,” International Journal of Wisdom Based Computing, vol. 1, no. 1, pp. 35–49, 2011. View at Google Scholar
  37. N. Liang, G. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411–1423, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. G. Huang, L. Chen, and C. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892, 2006. View at Publisher · View at Google Scholar · View at Scopus
  39. C. Schüldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local SVM approach,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), pp. 32–36, Cambridge, UK, August 2004. View at Publisher · View at Google Scholar · View at Scopus
  40. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2247–2253, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. B. Yousefi, C. K. Loo, and A. Memariani, “Biological inspired human action recognition,” in Proceedings of the IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS '13), pp. 58–65, IEEE, 2013. View at Google Scholar
  42. H. Jhuang, T. Serre, L. Wolf, and T. Poggio, “A biologically inspired system for action recognition,” in Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV '07), pp. 1253–1260, Rio de Janeiro, Brazil, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. Z. Zhang and D. Tao, “Slow feature analysis for human action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 436–450, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. N. Naveen, V. Ravi, C. R. Rao, and N. Chauhan, “Differential evolution trained radial basis function network: application to bankruptcy prediction in banks,” International Journal of Bio-Inspired Computation, vol. 2, no. 3-4, pp. 222–232, 2010. View at Publisher · View at Google Scholar · View at Scopus
  45. J. C. Niebles, H. Wang, and L. Fei-Fei, “Unsupervised learning of human action categories using spatial-temporal words,” International Journal of Computer Vision, vol. 79, no. 3, pp. 299–318, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. Y. Wang and G. Mori, “Human action recognition by semilatent topic models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1762–1774, 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. A. A. Efros, A. C. Berg, G. Mori, and J. Malik, “Recognizing action at a distance,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 726–733, Nice, France, October 2003. View at Scopus
  48. S. M. Thurman and H. Lu, “Complex interactions between spatial, orientation, and motion cues for biological motion perception across visual space,” Journal of Vision, vol. 13, no. 2, article 8, 2013. View at Publisher · View at Google Scholar · View at Scopus
  49. C. Distler, D. Boussaoud, R. Desimone, and L. G. Ungerleider, “Cortical connections of inferior temporal area TEO in macaque monkeys,” Journal of Comparative Neurology, vol. 334, no. 1, pp. 125–150, 1993. View at Publisher · View at Google Scholar · View at Scopus
  50. D. J. Felleman and D. C. Van Essen, “Distributed hierarchical processing in the primate cerebral cortex,” Cerebral Cortex, vol. 1, no. 1, pp. 1–47, 1991. View at Google Scholar · View at Scopus
  51. S. R. Lehky, X. Peng, C. J. McAdams, and A. B. Sereno, “Spatial modulation of primate inferotemporal responses by eye position,” PLoS ONE, vol. 3, no. 10, Article ID e3492, 2008. View at Publisher · View at Google Scholar · View at Scopus