The Nature of the Complicated Fabric Textures: How to Represent in Primary Visual Cortex
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:70061", author = "J. L. Liu and L. Wang and B. Zhu and J. Zhou and W. D. Gao", title = "The Nature of the Complicated Fabric Textures: How to Represent in Primary Visual Cortex", 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.", volume = "9", number = "5", pages = "1313-6", }