The Nature of the Complicated Fabric Textures: How to Represent in Primary Visual Cortex
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The Nature of the Complicated Fabric Textures: How to Represent in Primary Visual Cortex

Authors: J. L. Liu, L. Wang, B. Zhu, J. Zhou, W. D. Gao

Abstract:

Fabric textures are very common in our daily life. However, the representation of fabric textures has never been explored from neuroscience view. Theoretical studies suggest that primary visual cortex (V1) uses a sparse code to efficiently represent natural images. However, how the simple cells in V1 encode the artificial textures is still a mystery. So, here we will take fabric texture as stimulus to study the response of independent component analysis that is established to model the receptive field of simple cells in V1. We choose 140 types of fabrics to get the classical fabric textures as materials. Experiment results indicate that the receptive fields of simple cells have obvious selectivity in orientation, frequency and phase when drifting gratings are used to determine their tuning properties. Additionally, the distribution of optimal orientation and frequency shows that the patch size selected from each original fabric image has a significant effect on the frequency selectivity.

Keywords: Fabric Texture, Receptive Filed, Simple Cell, Spare Coding.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1106855

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References:


[1] J. Eichhorn, F. Sinz, and M. Bethge, “Natural image coding in V1: how much use is orientation selectivity?,” PLoS Comput. Biol., vol. 5, no. 4, p. e1000336, Apr. 2009.
[2] B. a Olshausen and D. J. Field, “Natural image statistics and efficient coding.,” Network, vol. 7, no. 2, pp. 333–9, May 1996.
[3] P. O. Hoyer and A. Hyvarinen, “Sparse coding of natural contours,” Neurocomputing, vol. 46, pp. 459–466, 2002.
[4] J. Hurri and P. O. Hoyer, “Natural Image Statistics,” 2009.
[5] 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. 607–9, Jun. 1996.
[6] D. E. Mitchell, J. Kennie, D. S. Schwarzkopf, and F. Sengpiel, “Daily mixed visual experience that prevents amblyopia in cats does not always allow the development of good binocular depth perception.,” J. Vis., vol. 9, no. 5, pp. 22.1–7, Jan. 2009.
[7] S. P. MacEvoy, T. R. Tucker, and D. Fitzpatrick, “A precise form of divisive suppression supports population coding in the primary visual cortex.,” Nat. Neurosci., vol. 12, no. 5, pp. 637–45, May 2009.
[8] A. Pooresmaeili, J. Poort, A. Thiele, and P. R. Roelfsema, “Separable codes for attention and luminance contrast in the primary visual cortex.,” J. Neurosci., vol. 30, no. 38, pp. 12701–12711, Sep. 2010.
[9] O. Shriki, A. Kohn, and M. Shamir, “Fast coding of orientation in primary visual cortex.,” PLoS Comput. Biol., vol. 8, no. 6, p. e1002536, Jan. 2012.
[10] G. Basalyga, M. a Montemurro, and T. Wennekers, “Information coding in a laminar computational model of cat primary visual cortex.,” J. Comput. Neurosci., vol. 34, no. 2, pp. 273–83, Apr. 2013.
[11] W. Lee and M. Sato, “Visual perception of texture of textiles,” Color Res. Appl., vol. 26, no. 6, pp. 469–477, Dec. 2001.
[12] A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications.,” Neural Networks, vol. 13, no. 4–5, pp. 411–30, 2000.
[13] L. Zhang and J. Mei, “Shaping up simple cell’s receptive field of animal vision by ICA and its application in navigation system,” Neural Networks, vol. 16, pp. 609–615, 2003.
[14] A. Lörincz, Z. Palotai, and G. Szirtes, “Efficient sparse coding in early sensory processing: lessons from signal recovery.,” PLoS Comput. Biol., vol. 8, no. 3, p. e1002372, Jan. 2012.
[15] A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis.,” IEEE Trans. neural networks, vol. 10, no. 3, pp. 626–34, Jan. 1999.