Abstract

In general, viewers are more attracted to local features in images at a shorter viewing distance and to global features in images at a longer viewing distance. However, numerical analysis of the effect of viewing distance on human texture perception and how the perception of global and local changes under certain conditions are still undetermined. In this paper, we present statistical prediction of the relationship between the domination ratio of global and local features and the viewing distances under the control of several factors, using the logistic regression model. We synthesized textures by separately controlling global and local textural features using a texture model based on mathematical morphology, namely the primitive, grain, and point configuration texture model. Visual sensory tests were carried out on 80 subjects during two sets of experiments. The collected data were statistically analyzed using logistic regression and Akaike information criteria. Besides the main factor of viewing distance, the factors including gender, changing the order of viewing positions, and prior knowledge were also shown quantitatively to have significant influence on human texture perception. Our results showed that (1) local features of a texture were more attractive to females than males, (2) the first impression might have affected subsequent decisions in texture perception, and (3) subjects who had prior knowledge (supervised) were more sensitive to the changes in global and local dominance. (4) Regarding the interactions of the factors, prior knowledge reduced the effects of individual differences and perception condition differences on human texture perception. This study is dedicated to the construction of numerical relationships between viewing distance and human texture perception as well as to cognitive investigation of biases in global and local perceptions.

© 2013 Optical Society of America

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References

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  1. M. Petrou and P. Garicia-Sevilla, Image Processing: Deal with Texture (Wiley, 2006).
  2. S. Morioka, M. Kamijo, S. Hosoya, T. Sadoyama, and Y. Shimizu, “Mathematical modeling for texture of fabrics using multi image,” IEICE Tech. Rep., Vol. 100 (IEICE, 2000), pp. 39–46.
  3. B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh, “Classifying materials in the real world,” Image Vision Comput. 28, 150–163 (2010).
    [CrossRef]
  4. R. Silvers, Photomosaics (Henry Holt, 1997).
  5. A. Oliva, A. Torralba, and P. G. Schyns, “Hybrid images,” ACM Trans. Graph. 25, 527–530 (2006).
    [CrossRef]
  6. H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Trans. Syst. Man Cybern. A 8, 460–473 (1978).
    [CrossRef]
  7. M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Trans. Syst. Man Cybern. A 19, 1264–1274 (1989).
    [CrossRef]
  8. A. R. Rao, “Identifying high level features of texture perception,” Comput. Vision Graph. Image Process. 55, 218–233 (1993).
  9. A. R. Rao and G. L. Lohse, “Towards a texture naming system: identifying relevant dimensions of texture,” Vision Res. 36, 1649–1669 (1996).
    [CrossRef]
  10. C. Muraki, A. Asano, L. Li, and T. Fujimoto, “Morphological texture manipulation for the evaluation of human visual sensibility,” Kansei Eng. Int. J. 10, 11–18 (2010).
  11. L. Li, A. Asano, and C. Muraki Asano, “Statistical analysis of human visual impressions on morphological image manipulation of gray scale textures,” Opt. Rev. 17, 90–96 (2010).
    [CrossRef]
  12. R. Kimchi, “Primacy of wholistic processing and global/local praradigm: a critical review,” Psychol. Bull. 112, 24–38 (1992).
    [CrossRef]
  13. M. A. Boeschoten, C. Kemner, J. L. Kenemans, and H. van Engeland, “The relationship between local and global processing and the processing of high and low spatial frequencies studied by event-related potentials and source modeling,” Cogn. Brain Res. 24, 228–236 (2005).
    [CrossRef]
  14. J. Davidoof, E. Fonteneau, and J. Fagot, “Local and global processing: observations from a remote culture,” Cognition 108, 702–709 (2008).
    [CrossRef]
  15. E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
    [CrossRef]
  16. D. Navon, “Forest before trees: the precedence of global features in visual perception,” Cogn. Psychol. 9, 353–383 (1977).
  17. C. L. Dulaney and W. Marks, “The effects of training and transfer on global/local processing,” Acta Psychol. 125, 203–220 (2007).
    [CrossRef]
  18. D. Pena, M. J. Contreras, P. C. Shih, and J. Santacreu, “Solution strategies as possible explanations of individual and sex differences in a dynamic spatial task,” Acta Psychol. 128, 1–14 (2008).
    [CrossRef]
  19. L. Li, A. Asano, and C. Muraki Asano, “Analysis of the effect of the viewing distance in texture perception using morphological and statistical model,” in Proceedings of the 2009 International Workshop on Smart Info-Media Systems in Asia (IEICE, 2009), pp. 125–130.
  20. J. Serra, Image Analysis and Mathematical Morphology, Vol. 2Technical Advances (Academic, 1988).
  21. P. Soille, Morphological Image Analysis, 2nd ed. (Springer, 2003).
  22. H. J. A. M. Heijmans, Morphological Image Operators (Academic, 1994).
  23. A. Asano, T. Ohkubo, M. Muneyasu, and T. Hinamoto, “Primitive and point configuration texture model and primitive estimation using mathematical morphology,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), pp. 178–185.
  24. R. Kimchi, R. Amishav, and A. Sulitzeanu-Kenan, “Gender differences in global/local perception? Evidence from orientation and shape judgments,” Acta Psychol. 131, 64–71 (2008).
  25. H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control 19, 716–723 (1974).
    [CrossRef]

2010 (4)

B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh, “Classifying materials in the real world,” Image Vision Comput. 28, 150–163 (2010).
[CrossRef]

C. Muraki, A. Asano, L. Li, and T. Fujimoto, “Morphological texture manipulation for the evaluation of human visual sensibility,” Kansei Eng. Int. J. 10, 11–18 (2010).

L. Li, A. Asano, and C. Muraki Asano, “Statistical analysis of human visual impressions on morphological image manipulation of gray scale textures,” Opt. Rev. 17, 90–96 (2010).
[CrossRef]

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

2008 (3)

J. Davidoof, E. Fonteneau, and J. Fagot, “Local and global processing: observations from a remote culture,” Cognition 108, 702–709 (2008).
[CrossRef]

D. Pena, M. J. Contreras, P. C. Shih, and J. Santacreu, “Solution strategies as possible explanations of individual and sex differences in a dynamic spatial task,” Acta Psychol. 128, 1–14 (2008).
[CrossRef]

R. Kimchi, R. Amishav, and A. Sulitzeanu-Kenan, “Gender differences in global/local perception? Evidence from orientation and shape judgments,” Acta Psychol. 131, 64–71 (2008).

2007 (1)

C. L. Dulaney and W. Marks, “The effects of training and transfer on global/local processing,” Acta Psychol. 125, 203–220 (2007).
[CrossRef]

2006 (1)

A. Oliva, A. Torralba, and P. G. Schyns, “Hybrid images,” ACM Trans. Graph. 25, 527–530 (2006).
[CrossRef]

2005 (1)

M. A. Boeschoten, C. Kemner, J. L. Kenemans, and H. van Engeland, “The relationship between local and global processing and the processing of high and low spatial frequencies studied by event-related potentials and source modeling,” Cogn. Brain Res. 24, 228–236 (2005).
[CrossRef]

1996 (1)

A. R. Rao and G. L. Lohse, “Towards a texture naming system: identifying relevant dimensions of texture,” Vision Res. 36, 1649–1669 (1996).
[CrossRef]

1993 (1)

A. R. Rao, “Identifying high level features of texture perception,” Comput. Vision Graph. Image Process. 55, 218–233 (1993).

1992 (1)

R. Kimchi, “Primacy of wholistic processing and global/local praradigm: a critical review,” Psychol. Bull. 112, 24–38 (1992).
[CrossRef]

1989 (1)

M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Trans. Syst. Man Cybern. A 19, 1264–1274 (1989).
[CrossRef]

1978 (1)

H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Trans. Syst. Man Cybern. A 8, 460–473 (1978).
[CrossRef]

1977 (1)

D. Navon, “Forest before trees: the precedence of global features in visual perception,” Cogn. Psychol. 9, 353–383 (1977).

1974 (1)

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control 19, 716–723 (1974).
[CrossRef]

Aalders, R.

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Akaike, H.

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control 19, 716–723 (1974).
[CrossRef]

Amadasun, M.

M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Trans. Syst. Man Cybern. A 19, 1264–1274 (1989).
[CrossRef]

Amishav, R.

R. Kimchi, R. Amishav, and A. Sulitzeanu-Kenan, “Gender differences in global/local perception? Evidence from orientation and shape judgments,” Acta Psychol. 131, 64–71 (2008).

Asano, A.

C. Muraki, A. Asano, L. Li, and T. Fujimoto, “Morphological texture manipulation for the evaluation of human visual sensibility,” Kansei Eng. Int. J. 10, 11–18 (2010).

L. Li, A. Asano, and C. Muraki Asano, “Statistical analysis of human visual impressions on morphological image manipulation of gray scale textures,” Opt. Rev. 17, 90–96 (2010).
[CrossRef]

L. Li, A. Asano, and C. Muraki Asano, “Analysis of the effect of the viewing distance in texture perception using morphological and statistical model,” in Proceedings of the 2009 International Workshop on Smart Info-Media Systems in Asia (IEICE, 2009), pp. 125–130.

A. Asano, T. Ohkubo, M. Muneyasu, and T. Hinamoto, “Primitive and point configuration texture model and primitive estimation using mathematical morphology,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), pp. 178–185.

Boeschoten, M. A.

M. A. Boeschoten, C. Kemner, J. L. Kenemans, and H. van Engeland, “The relationship between local and global processing and the processing of high and low spatial frequencies studied by event-related potentials and source modeling,” Cogn. Brain Res. 24, 228–236 (2005).
[CrossRef]

Caputo, B.

B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh, “Classifying materials in the real world,” Image Vision Comput. 28, 150–163 (2010).
[CrossRef]

Contreras, M. J.

D. Pena, M. J. Contreras, P. C. Shih, and J. Santacreu, “Solution strategies as possible explanations of individual and sex differences in a dynamic spatial task,” Acta Psychol. 128, 1–14 (2008).
[CrossRef]

Davidoof, J.

J. Davidoof, E. Fonteneau, and J. Fagot, “Local and global processing: observations from a remote culture,” Cognition 108, 702–709 (2008).
[CrossRef]

Davies, A. A.

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Dulaney, C. L.

C. L. Dulaney and W. Marks, “The effects of training and transfer on global/local processing,” Acta Psychol. 125, 203–220 (2007).
[CrossRef]

Eklundh, J.-O.

B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh, “Classifying materials in the real world,” Image Vision Comput. 28, 150–163 (2010).
[CrossRef]

Fagot, J.

J. Davidoof, E. Fonteneau, and J. Fagot, “Local and global processing: observations from a remote culture,” Cognition 108, 702–709 (2008).
[CrossRef]

Fernando, D.

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Fonteneau, E.

J. Davidoof, E. Fonteneau, and J. Fagot, “Local and global processing: observations from a remote culture,” Cognition 108, 702–709 (2008).
[CrossRef]

Fritz, M.

B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh, “Classifying materials in the real world,” Image Vision Comput. 28, 150–163 (2010).
[CrossRef]

Fujimoto, T.

C. Muraki, A. Asano, L. Li, and T. Fujimoto, “Morphological texture manipulation for the evaluation of human visual sensibility,” Kansei Eng. Int. J. 10, 11–18 (2010).

Garicia-Sevilla, P.

M. Petrou and P. Garicia-Sevilla, Image Processing: Deal with Texture (Wiley, 2006).

Hayman, E.

B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh, “Classifying materials in the real world,” Image Vision Comput. 28, 150–163 (2010).
[CrossRef]

Heijmans, H. J. A. M.

H. J. A. M. Heijmans, Morphological Image Operators (Academic, 1994).

Hinamoto, T.

A. Asano, T. Ohkubo, M. Muneyasu, and T. Hinamoto, “Primitive and point configuration texture model and primitive estimation using mathematical morphology,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), pp. 178–185.

Hosoya, S.

S. Morioka, M. Kamijo, S. Hosoya, T. Sadoyama, and Y. Shimizu, “Mathematical modeling for texture of fabrics using multi image,” IEICE Tech. Rep., Vol. 100 (IEICE, 2000), pp. 39–46.

Kamijo, M.

S. Morioka, M. Kamijo, S. Hosoya, T. Sadoyama, and Y. Shimizu, “Mathematical modeling for texture of fabrics using multi image,” IEICE Tech. Rep., Vol. 100 (IEICE, 2000), pp. 39–46.

Kemner, C.

M. A. Boeschoten, C. Kemner, J. L. Kenemans, and H. van Engeland, “The relationship between local and global processing and the processing of high and low spatial frequencies studied by event-related potentials and source modeling,” Cogn. Brain Res. 24, 228–236 (2005).
[CrossRef]

Kenemans, J. L.

M. A. Boeschoten, C. Kemner, J. L. Kenemans, and H. van Engeland, “The relationship between local and global processing and the processing of high and low spatial frequencies studied by event-related potentials and source modeling,” Cogn. Brain Res. 24, 228–236 (2005).
[CrossRef]

Kimchi, R.

R. Kimchi, R. Amishav, and A. Sulitzeanu-Kenan, “Gender differences in global/local perception? Evidence from orientation and shape judgments,” Acta Psychol. 131, 64–71 (2008).

R. Kimchi, “Primacy of wholistic processing and global/local praradigm: a critical review,” Psychol. Bull. 112, 24–38 (1992).
[CrossRef]

King, R.

M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Trans. Syst. Man Cybern. A 19, 1264–1274 (1989).
[CrossRef]

Leung, H.

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Li, L.

C. Muraki, A. Asano, L. Li, and T. Fujimoto, “Morphological texture manipulation for the evaluation of human visual sensibility,” Kansei Eng. Int. J. 10, 11–18 (2010).

L. Li, A. Asano, and C. Muraki Asano, “Statistical analysis of human visual impressions on morphological image manipulation of gray scale textures,” Opt. Rev. 17, 90–96 (2010).
[CrossRef]

L. Li, A. Asano, and C. Muraki Asano, “Analysis of the effect of the viewing distance in texture perception using morphological and statistical model,” in Proceedings of the 2009 International Workshop on Smart Info-Media Systems in Asia (IEICE, 2009), pp. 125–130.

Lohse, G. L.

A. R. Rao and G. L. Lohse, “Towards a texture naming system: identifying relevant dimensions of texture,” Vision Res. 36, 1649–1669 (1996).
[CrossRef]

Marks, W.

C. L. Dulaney and W. Marks, “The effects of training and transfer on global/local processing,” Acta Psychol. 125, 203–220 (2007).
[CrossRef]

McKone, E.

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Mori, S.

H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Trans. Syst. Man Cybern. A 8, 460–473 (1978).
[CrossRef]

Morioka, S.

S. Morioka, M. Kamijo, S. Hosoya, T. Sadoyama, and Y. Shimizu, “Mathematical modeling for texture of fabrics using multi image,” IEICE Tech. Rep., Vol. 100 (IEICE, 2000), pp. 39–46.

Muneyasu, M.

A. Asano, T. Ohkubo, M. Muneyasu, and T. Hinamoto, “Primitive and point configuration texture model and primitive estimation using mathematical morphology,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), pp. 178–185.

Muraki, C.

C. Muraki, A. Asano, L. Li, and T. Fujimoto, “Morphological texture manipulation for the evaluation of human visual sensibility,” Kansei Eng. Int. J. 10, 11–18 (2010).

Muraki Asano, C.

L. Li, A. Asano, and C. Muraki Asano, “Statistical analysis of human visual impressions on morphological image manipulation of gray scale textures,” Opt. Rev. 17, 90–96 (2010).
[CrossRef]

L. Li, A. Asano, and C. Muraki Asano, “Analysis of the effect of the viewing distance in texture perception using morphological and statistical model,” in Proceedings of the 2009 International Workshop on Smart Info-Media Systems in Asia (IEICE, 2009), pp. 125–130.

Navon, D.

D. Navon, “Forest before trees: the precedence of global features in visual perception,” Cogn. Psychol. 9, 353–383 (1977).

Ohkubo, T.

A. Asano, T. Ohkubo, M. Muneyasu, and T. Hinamoto, “Primitive and point configuration texture model and primitive estimation using mathematical morphology,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), pp. 178–185.

Oliva, A.

A. Oliva, A. Torralba, and P. G. Schyns, “Hybrid images,” ACM Trans. Graph. 25, 527–530 (2006).
[CrossRef]

Pena, D.

D. Pena, M. J. Contreras, P. C. Shih, and J. Santacreu, “Solution strategies as possible explanations of individual and sex differences in a dynamic spatial task,” Acta Psychol. 128, 1–14 (2008).
[CrossRef]

Petrou, M.

M. Petrou and P. Garicia-Sevilla, Image Processing: Deal with Texture (Wiley, 2006).

Platow, M. J.

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Rao, A. R.

A. R. Rao and G. L. Lohse, “Towards a texture naming system: identifying relevant dimensions of texture,” Vision Res. 36, 1649–1669 (1996).
[CrossRef]

A. R. Rao, “Identifying high level features of texture perception,” Comput. Vision Graph. Image Process. 55, 218–233 (1993).

Sadoyama, T.

S. Morioka, M. Kamijo, S. Hosoya, T. Sadoyama, and Y. Shimizu, “Mathematical modeling for texture of fabrics using multi image,” IEICE Tech. Rep., Vol. 100 (IEICE, 2000), pp. 39–46.

Santacreu, J.

D. Pena, M. J. Contreras, P. C. Shih, and J. Santacreu, “Solution strategies as possible explanations of individual and sex differences in a dynamic spatial task,” Acta Psychol. 128, 1–14 (2008).
[CrossRef]

Schyns, P. G.

A. Oliva, A. Torralba, and P. G. Schyns, “Hybrid images,” ACM Trans. Graph. 25, 527–530 (2006).
[CrossRef]

Serra, J.

J. Serra, Image Analysis and Mathematical Morphology, Vol. 2Technical Advances (Academic, 1988).

Shih, P. C.

D. Pena, M. J. Contreras, P. C. Shih, and J. Santacreu, “Solution strategies as possible explanations of individual and sex differences in a dynamic spatial task,” Acta Psychol. 128, 1–14 (2008).
[CrossRef]

Shimizu, Y.

S. Morioka, M. Kamijo, S. Hosoya, T. Sadoyama, and Y. Shimizu, “Mathematical modeling for texture of fabrics using multi image,” IEICE Tech. Rep., Vol. 100 (IEICE, 2000), pp. 39–46.

Silvers, R.

R. Silvers, Photomosaics (Henry Holt, 1997).

Soille, P.

P. Soille, Morphological Image Analysis, 2nd ed. (Springer, 2003).

Sulitzeanu-Kenan, A.

R. Kimchi, R. Amishav, and A. Sulitzeanu-Kenan, “Gender differences in global/local perception? Evidence from orientation and shape judgments,” Acta Psychol. 131, 64–71 (2008).

Tamura, H.

H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Trans. Syst. Man Cybern. A 8, 460–473 (1978).
[CrossRef]

Torralba, A.

A. Oliva, A. Torralba, and P. G. Schyns, “Hybrid images,” ACM Trans. Graph. 25, 527–530 (2006).
[CrossRef]

van Engeland, H.

M. A. Boeschoten, C. Kemner, J. L. Kenemans, and H. van Engeland, “The relationship between local and global processing and the processing of high and low spatial frequencies studied by event-related potentials and source modeling,” Cogn. Brain Res. 24, 228–236 (2005).
[CrossRef]

Wickramariyaratne, T.

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Yamawaki, T.

H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Trans. Syst. Man Cybern. A 8, 460–473 (1978).
[CrossRef]

ACM Trans. Graph. (1)

A. Oliva, A. Torralba, and P. G. Schyns, “Hybrid images,” ACM Trans. Graph. 25, 527–530 (2006).
[CrossRef]

Acta Psychol. (3)

C. L. Dulaney and W. Marks, “The effects of training and transfer on global/local processing,” Acta Psychol. 125, 203–220 (2007).
[CrossRef]

D. Pena, M. J. Contreras, P. C. Shih, and J. Santacreu, “Solution strategies as possible explanations of individual and sex differences in a dynamic spatial task,” Acta Psychol. 128, 1–14 (2008).
[CrossRef]

R. Kimchi, R. Amishav, and A. Sulitzeanu-Kenan, “Gender differences in global/local perception? Evidence from orientation and shape judgments,” Acta Psychol. 131, 64–71 (2008).

Cogn. Brain Res. (1)

M. A. Boeschoten, C. Kemner, J. L. Kenemans, and H. van Engeland, “The relationship between local and global processing and the processing of high and low spatial frequencies studied by event-related potentials and source modeling,” Cogn. Brain Res. 24, 228–236 (2005).
[CrossRef]

Cogn. Psychol. (1)

D. Navon, “Forest before trees: the precedence of global features in visual perception,” Cogn. Psychol. 9, 353–383 (1977).

Cognition (1)

J. Davidoof, E. Fonteneau, and J. Fagot, “Local and global processing: observations from a remote culture,” Cognition 108, 702–709 (2008).
[CrossRef]

Comput. Vision Graph. Image Process. (1)

A. R. Rao, “Identifying high level features of texture perception,” Comput. Vision Graph. Image Process. 55, 218–233 (1993).

IEEE Trans. Autom. Control (1)

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control 19, 716–723 (1974).
[CrossRef]

IEEE Trans. Syst. Man Cybern. A (2)

H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Trans. Syst. Man Cybern. A 8, 460–473 (1978).
[CrossRef]

M. Amadasun and R. King, “Textural features corresponding to textural properties,” IEEE Trans. Syst. Man Cybern. A 19, 1264–1274 (1989).
[CrossRef]

Image Vision Comput. (1)

B. Caputo, E. Hayman, M. Fritz, and J.-O. Eklundh, “Classifying materials in the real world,” Image Vision Comput. 28, 150–163 (2010).
[CrossRef]

Kansei Eng. Int. J. (1)

C. Muraki, A. Asano, L. Li, and T. Fujimoto, “Morphological texture manipulation for the evaluation of human visual sensibility,” Kansei Eng. Int. J. 10, 11–18 (2010).

Opt. Rev. (1)

L. Li, A. Asano, and C. Muraki Asano, “Statistical analysis of human visual impressions on morphological image manipulation of gray scale textures,” Opt. Rev. 17, 90–96 (2010).
[CrossRef]

Psychol. Bull. (1)

R. Kimchi, “Primacy of wholistic processing and global/local praradigm: a critical review,” Psychol. Bull. 112, 24–38 (1992).
[CrossRef]

Vis. Res. (1)

E. McKone, A. A. Davies, D. Fernando, R. Aalders, H. Leung, T. Wickramariyaratne, and M. J. Platow, “Asia has the global advantage: race and visual attention,” Vis. Res. 50, 1540–1549 (2010).
[CrossRef]

Vision Res. (1)

A. R. Rao and G. L. Lohse, “Towards a texture naming system: identifying relevant dimensions of texture,” Vision Res. 36, 1649–1669 (1996).
[CrossRef]

Other (8)

R. Silvers, Photomosaics (Henry Holt, 1997).

M. Petrou and P. Garicia-Sevilla, Image Processing: Deal with Texture (Wiley, 2006).

S. Morioka, M. Kamijo, S. Hosoya, T. Sadoyama, and Y. Shimizu, “Mathematical modeling for texture of fabrics using multi image,” IEICE Tech. Rep., Vol. 100 (IEICE, 2000), pp. 39–46.

L. Li, A. Asano, and C. Muraki Asano, “Analysis of the effect of the viewing distance in texture perception using morphological and statistical model,” in Proceedings of the 2009 International Workshop on Smart Info-Media Systems in Asia (IEICE, 2009), pp. 125–130.

J. Serra, Image Analysis and Mathematical Morphology, Vol. 2Technical Advances (Academic, 1988).

P. Soille, Morphological Image Analysis, 2nd ed. (Springer, 2003).

H. J. A. M. Heijmans, Morphological Image Operators (Academic, 1994).

A. Asano, T. Ohkubo, M. Muneyasu, and T. Hinamoto, “Primitive and point configuration texture model and primitive estimation using mathematical morphology,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), pp. 178–185.

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Figures (12)

Fig. 1.
Fig. 1.

Synthesized textures: (a) Skeleton of texture 1, (b) grain of texture 1, (c) synthesized texture 1, (d) skeleton of texture 2, (e) grain of texture 2, and (f) synthesized texture 2.

Fig. 2.
Fig. 2.

First scene used in the experiments.

Fig. 3.
Fig. 3.

Second scene used in the experiments.

Fig. 4.
Fig. 4.

Groups of subjects in the experiments (numbers in parentheses indicate the number of subjects in each group).

Fig. 5.
Fig. 5.

Procedures of the two sets of experiments.

Fig. 6.
Fig. 6.

Logistic regression: scene 1 versus scene 2. The error bars represent the 95% confidence intervals.

Fig. 7.
Fig. 7.

Far-to-near groups (nos. 1–4) versus near-to-far groups (nos. 5–8) in scene 1. The error bars represent the 95% confidence intervals.

Fig. 8.
Fig. 8.

Far-to-near groups (nos. 1–4) versus near-to-far groups (nos. 5–8) in scene 2. The error bars represent the 95% confidence intervals.

Fig. 9.
Fig. 9.

Unsupervised groups (nos. 1, 2, 5, and 6) versus supervised groups (nos. 3, 4, 7, and 8) in scene 1. The error bars represent the 95% confidence intervals.

Fig. 10.
Fig. 10.

Unsupervised groups (nos. 1, 2, 5, and 6) versus supervised groups (nos. 3, 4, 7, and 8) in scene 2. The error bars represent the 95% confidence intervals.

Fig. 11.
Fig. 11.

Male (groups nos. 1, 3, 5, and 7) versus female (groups nos. 2, 4, 6, and 8) in scene 2. The error bars represent the 95% confidence intervals.

Fig. 12.
Fig. 12.

Male groups (nos. 1, 3, and 7) versus female groups (nos. 2, 4, 6, and 8) in scene 2. The error bars represent the 95% confidence intervals.

Tables (8)

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Table 1. Visual Acuity Information of the Subjects

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Table 2. Angular Sizes (in Minutes of Arc) of Each White Square Displayed in Scene 1 and Scene 2 at Different Viewing Distances (in Centimeters)

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Table 3. Experimental Results

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Table 4. Experimental Results

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Table 5. Regression Coefficients and P -Values of Scenes 1 and 2

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Table 6. Comparison of Regression Coefficients and P -Values of Supervised and Unsupervised Groups for Scenes 1 and 2

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Table 7. Comparison of Regression Coefficients and P -Values of Far-to-Near and Near-to-Far Groups for Scenes 1 and 2

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Table 8. Comparison of Regression Coefficients and P -Values of Male and Female Groups for Scenes 1 and 2

Equations (14)

Equations on this page are rendered with MathJax. Learn more.

f ( Z ) = 1 1 + e Z ,
Z = β 0 + β 1 x 1 + β 2 x 2 + + β r x r ,
p = 1 1 + e β 0 β 1 D ,
A I C = 2 k 2 ln ( L ) ,
Z = β 0 + β 1 D + β 2 G + β 3 S + β 4 C ,
Z = { β 0 + β 1 D + β 2 G + β 3 C ( S = 1 ) , β 0 + β 1 D + β 2 G + β 3 C ( S = 0 ) .
Z = { β 0 + β 1 D + β 2 G + β 3 S ( C = 1 ) , β 0 + β 1 D + β 2 G + β 3 S ( C = 0 ) .
Z = { β 0 + β 1 D + β 2 C + β 3 S ( G = 1 ) , β 0 + β 1 D + β 2 C + β 3 S ( G = 0 ) .
X B = ( X ) B ,
X B = inf b B X ( x b ) ,
X B = sup b B X ( x b ) .
X = inf b B X ( x + b ) .
S K n ( X , B ) = ( X n ) ( X n ) B ,
T = sup n = 0 N B n Φ n ,

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