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Image understanding, attention and human early visual cortex

Frontiers of Electrical and Electronic Engineering volume 7, pages 85–93 (2012)Cite this article

Abstract

This paper reviews our recent fMRI and psychophysical finding on: 1) perceived size representation in V1; 2) border ownership representation in V2; and 3) neural processing of partially occluded face. These findings demonstrate that the human early visual cortex not only performs local feature analyses, but also contributes significantly to high-level visual computation with assistance of attention-enabled cortical feedback. Moreover, by taking advantage of recent findings on early visual cortex from neuroscience and cognitive science, we build a biologically plausible attention model that can well predict human scanpaths on natural images.

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References

  1. Wandell B A, Dumoulin S O, Brewer A A. Visual field maps in human cortex. Neuron, 2007, 56(2): 366–383

    Article  Google Scholar 

  2. Fang F, Boyaci H, Kersten D, Murray S O. Attention-dependent representation of a size illusion in human V1. Current Biology, 2008, 18(21): 1707–1712

    Article  Google Scholar 

  3. Wojciulik E, Kanwisher N, Driver J. Covert visual attention modulates face-specific activity in the human fusiform gyrus: fMRI study. Journal of Neurophysiology, 1998, 79(3): 1574–1578

    Google Scholar 

  4. Murray S O, He S. Contrast invariance in the human lateral occipital complex depends on attention. Current Biology, 2006, 16(6): 606–611

    Article  Google Scholar 

  5. Nakayama K, Shimojo S, Silverman G H. Stereoscopic depth: Its relation to image segmentation, grouping, and the recognition of occluded objects. Perception, 1989, 18(1): 55–68

    Article  Google Scholar 

  6. Driver J, Baylis G C. Edge-assignment and figure-ground segmentation in short-term visual matching. Cognitive Psychology, 1996, 31(3): 248–306

    Article  Google Scholar 

  7. Lee T S, Mumford D, Romero R, Lamme V A F. The role of the primary visual cortex in higher level vision. Vision Research, 1998, 38(15–16): 2429–2454

    Article  Google Scholar 

  8. Zhou H, Friedman H S, von der Heydt R. Coding of border ownership in monkey visual cortex. Journal of Neuroscience, 2000, 20(17): 6594–6611

    Google Scholar 

  9. Qiu F T, von der Heydt R. Figure and ground in the visual cortex: V2 combines stereoscopic cues with Gestalt rules. Neuron, 2005, 47(1): 155–166

    Article  Google Scholar 

  10. Kourtzi Z, Kanwisher N. Representation of perceived object shape by the human lateral occipital complex. Science, 2001, 293(5534): 1506–1509

    Article  Google Scholar 

  11. Andrews T J, Schluppeck D, Homfray D, Matthews P, Blakemore C. Activity in the fusiform gyrus predicts conscious perception of Rubin’s vase-face illusion. NeuroImage, 2002, 17(2): 890–901

    Article  Google Scholar 

  12. Fang F, Murray S O, Kersten D J, He S. Orientation-tuned fMRI adaptation in human visual cortex. Journal of Neurophysiology, 2005, 94(6): 4188–4195

    Article  Google Scholar 

  13. Fang F, Boyaci H, Kersten D. Border ownership selectivity in human early visual cortex and its modulation by attention. Journal of Neuroscience, 2009, 29(2): 460–465

    Article  Google Scholar 

  14. Albright T D, Stoner G R. Contextual influences on visual processing. Annual Review of Neuroscience, 2002, 25(1): 339–379

    Article  Google Scholar 

  15. Grill-Spector K, Malach R. The human visual cortex. Annual Review of Neuroscience, 2004, 27(1): 649–677

    Article  Google Scholar 

  16. Kanwisher N. Functional specificity in the human brain: A window into the functional architecture of the mind. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(25): 11163–11170

    Article  Google Scholar 

  17. Stanley D A, Rubin N. fMRI activation in response to illusory contours and salient regions in the human lateral occipital complex. Neuron, 2003, 37(2): 323–331

    Article  Google Scholar 

  18. Hegde J, Fang F, Murray S O, Kersten D. Preferential responses to occluded objects in the human visual cortex. Journal of Vision, 2008, 8(4): 16-1–16-16

    Article  Google Scholar 

  19. Grutzner C, Uhlhaas P J, Genc E, Kohler A, Singer W, Wibral M. Neuroelectromagnetic correlates of perceptual closure processes. Journal of Neuroscience, 2010, 30(24): 8342–8352

    Article  Google Scholar 

  20. Sugita Y. Grouping of image fragments in primary visual cortex. Nature, 1999, 401(6750): 269–272

    Article  Google Scholar 

  21. Pillow J, Rubin N. Perceptual completion across the vertical meridian and the role of early visual cortex. Neuron, 2002, 33(5): 805–813

    Article  Google Scholar 

  22. Bakin J S, Nakayama K, Gilbert C D. Visual responses in monkey areas V1 and V2 to three-dimensional surface configurations. Journal of Neuroscience, 2000, 20(21): 8188–8198

    Google Scholar 

  23. Lerner Y, Hendler T, Malach R. Object-completion effects in the human lateral occipital complex. Cerebral Cortex, 2002, 12(2): 163–177

    Article  Google Scholar 

  24. Murray R F, Sekuler A B, Bennett P J. Time course of amodal completion revealed by a shape discrimination task. Psychonomic Bulletin & Review, 2001, 8(4): 713–720

    Article  Google Scholar 

  25. Chen J, Liu B, Chen B, Fang F. Time course of amodal completion in face perception. Vision Research, 2009, 49(7): 752–758

    Article  Google Scholar 

  26. Bar M, Tootell R B, Schacter D, Greve D, Fischl B, Mendola J, Rosen B, Dale A. Cortical mechanisms specific to explicit visual object recognition. Neuron, 2001, 29(2): 529–535

    Article  Google Scholar 

  27. Lamme V A F, Zipser K, Spekreijse H. Masking interrupts figure-ground signals in V1. Journal of Cognitive Neuroscience, 2002, 14(7): 1044–1053

    Article  Google Scholar 

  28. Chen J, Zhou T, Yang H, Fang F. Cortical dynamics underlying face completion in human visual system. Journal of Neuroscience, 2010, 30(49): 16692–16698

    Article  Google Scholar 

  29. Fang F, He S. Viewer-centered object representation in the human visual system revealed by viewpoint aftereffect. Neuron, 2005, 45(5): 793–800

    Article  MathSciNet  Google Scholar 

  30. Wang W, Chen C, Wang Y, Jiang T, Fang F, Yao Y. Simulating human saccadic scanpath on natural images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2011, 441–448

  31. Olshausen B, Field D. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 1996, 381(6583): 607–609

    Article  Google Scholar 

  32. Wang W, Wang Y, Huang Q, Gao W. Measuring visual saliency by site entropy rate. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 2368–2375

  33. Itti L, Koch C, Niebur E. A model of saliency based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254–1259

    Article  Google Scholar 

  34. Yao J G, Gao X, Yan H M, Li C Y. Field of attention for instantaneous object recognition. PLoS ONE, 2011, 6(1): e16343

    Article  Google Scholar 

  35. Hou X, Zhang L. Dynamic visual attention: Searching for coding length increments. Advances in Neural Information Processing Systems, 2008, 21: 681–688

    Google Scholar 

  36. Sauer T, Yorke J, Casdagli M. Embedology. Journal of Statistical Physics, 1991, 65(3–4): 579–616

    Article  MathSciNet  MATH  Google Scholar 

  37. Bruce N, Tsotsos J. Saliency based on information maximization. Advances in Neural Information Processing Systems, 2006, 18: 155–162

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Psychology, Peking University, Beijing, 100871, China

    Fang Fang

  2. Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, 100871, China

    Fang Fang & Yizhou Wang

  3. Center for Life Sciences, Peking University, Beijing, 100871, China

    Fang Fang

  4. National Engineering Lab for Video Technology, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Yizhou Wang

Authors
  1. Fang Fang
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  2. Yizhou Wang
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Corresponding authors

Correspondence to Fang Fang or Yizhou Wang.

Additional information

Fang FANG is a professor in the Department of Psychology at Peking University, Beijing, China. He earned his B.S. degree in psychology and M.E. degree in signal and information processing at Peking University. From 2001 to 2007, he did his Ph.D. and postdoctoral training in cognitive and biological psychology at the University of Minnesota at Twin Cities. He then moved back to Peking University and started his own lab as principle investigator. His research has been dedicated to human visual perception, attention and awareness, using functional brain imaging, psychophysics and computational modeling. He has authored or co-authored about 40 research papers published in prestigious journals, including Nature Neuroscience, Neuron, Current Biology, Proceedings of the National Academy of Sciences of the United States of America (PNAS), Journal of Neuroscience. He received the National Distinguished Young Scientist Award from the National Natural Science Foundation of China in 2009 and the National Award for Youth in Science and Technology from the China Association for Science and Technology in 2011. He serves as editorial board member of Experimental Brain Research and Frontiers in Perception Science.

Yizhou WANG is a professor of Department of Computer Science at Peking University, Beijing, China. He is a vice director of Institute of Digital Media at Peking University, and the director of New Media Lab of National Engineering Lab for Video Technology. He received his Bachelor’s degree in electrical engineering from Tsinghua University, Beijing, China, in 1996, and his Ph.D. degree in computer science from University of California at Los Angeles (UCLA) in 2005. He worked at Xerox Palo Alto Research Center (Xerox PARC) as a research scientist from 2005 to 2007. Dr. Wang’s research interests include computer vision, statistical modeling and learning, and digital visual arts.

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Fang, F., Wang, Y. Image understanding, attention and human early visual cortex. Front. Electr. Electron. Eng. 7, 85–93 (2012). https://doi.org/10.1007/s11460-012-0184-0

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  • DOI: https://doi.org/10.1007/s11460-012-0184-0

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