Abstract

We examined how well we can recover surface-reflectance properties from shading patterns under changes in surface shape. The stimulus we used was a square surface modulated in depth by a low-pass-filtered random field and rendered by the Phong illumination model [Commun. ACM 18, 311 (1975)]. Two different surface images (target and match) were presented side by side, with either the viewing direction or the surface-normal direction rotating around the horizontal axis. The target shape was manipulated by changing the spatial spectrum, and the target reflectance was manipulated by changing the diffuse-reflection coefficient and the specular-reflection exponent (shininess) of the Phong model. The shape parameters of the match stimulus were fixed, but its reflectance parameters were under the control of subjects, who had to make the apparent reflectance of the two surfaces as similar as possible. The results showed that the constant error (difference between simulated and matched values) was large except when the two surfaces had the same shape parameters or when they differed only in scale. The pattern of the constant errors and response variabilities suggests that the judgments of the subjects were based on the similarity of the luminance histogram of the surface image. Our results demonstrate a limitation of surface-reflectance constancy for changes in shape and the importance of image-based information in reflectance judgments. The results are discussed in relation to previous studies that showed effects of spatial layout on surface-reflectance perception.

© 1998 Optical Society of America

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References

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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
  19. F. E. Pollick, S. Nishida, Y. Koike, M. Kawato, “Perceived motion in structure from motion: pointing responses to the axis of rotation,” Percept. Psychophys. 56, 91–109 (1994).
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    [CrossRef] [PubMed]
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1996

A. Johnston, W. Curran, “Investigating shape-from-shading illusions using solid objects,” Vision Res. 36, 2827–2835 (1996).
[CrossRef] [PubMed]

D. Kersten, A. C. Hurlbert, “Discounting the color of mutual illumination: a 3D-shape-induced color phenomenon,” Invest. Ophthalmol. Visual Sci. 37, S1065 (1996).

1994

F. E. Pollick, S. Nishida, Y. Koike, M. Kawato, “Perceived motion in structure from motion: pointing responses to the axis of rotation,” Percept. Psychophys. 56, 91–109 (1994).
[CrossRef] [PubMed]

A. Johnston, P. J. Passmore, “Independent encoding of surface orientation and surface curvature,” Vision Res. 34, 3005–3012 (1994).
[CrossRef] [PubMed]

1993

E. H. Adelson, “Perceptual organization and the judgement of brightness,” Science 262, 2042–2044 (1993).
[CrossRef] [PubMed]

1992

1991

D. C. Knill, D. Kersten, “Apparent surface curvature affects lightness perception,” Nature (London) 351, 228–230 (1991).
[CrossRef]

1990

A. Blake, H. Bülthoff, “Does the brain know the physics of specular reflection?” Nature (London) 343, 165–168 (1990).
[CrossRef]

J. T. Todd, P. Bressan, “The perception of 3-dimensional affine structure from minimal apparent motion sequences,” Percept. Psychophys. 48, 419–430 (1990).
[CrossRef] [PubMed]

1989

J. T. Todd, F. D. Reichel, “Ordinal structure in the vi-sual perception and cognition of smoothly curved surfaces,” Psychol. Rev. 96, 643–657 (1989).
[CrossRef] [PubMed]

1986

E. H. Land, “Recent advances in retinex theory,” Vision Res. 26, 7–22 (1986).
[CrossRef] [PubMed]

1984

A. Gilchrist, A. Jacobsen, “Perception of lightness and illumination in a world of one reflectance,” Perception 13, 5–19 (1984).
[PubMed]

1980

A. L. Gilchrist, “When does perceived lightness depend on perceived spatial arrangement?” Percept. Psychophys. 28, 527–538 (1980).
[CrossRef] [PubMed]

1979

A. L. Gilchrist, “The perception of surface blacks and whites,” Sci. Am. 240, 112–124 (1979).
[CrossRef] [PubMed]

1977

A. L. Gilchrist, “Perceived lightness depends on perceived spatial arrangement,” Science 195, 185–187 (1977).
[CrossRef] [PubMed]

1975

B. T. Phong, “Illumination for computer generated pictures,” Commun. ACM 18, 311–317 (1975).
[CrossRef]

Adelson, E. H.

E. H. Adelson, “Perceptual organization and the judgement of brightness,” Science 262, 2042–2044 (1993).
[CrossRef] [PubMed]

Blake, A.

A. Blake, H. Bülthoff, “Does the brain know the physics of specular reflection?” Nature (London) 343, 165–168 (1990).
[CrossRef]

Brainard, D. H.

Bressan, P.

J. T. Todd, P. Bressan, “The perception of 3-dimensional affine structure from minimal apparent motion sequences,” Percept. Psychophys. 48, 419–430 (1990).
[CrossRef] [PubMed]

Bülthoff, H.

A. Blake, H. Bülthoff, “Does the brain know the physics of specular reflection?” Nature (London) 343, 165–168 (1990).
[CrossRef]

Chubb, C.

C. Chubb, M. Landy, “Orthogonal distribution analysis: a new approach to the study of texture perception,” in Computational Models of Visual Processing, M. S. Landy, J. A. Movshon, eds. (MIT Press, Cambridge, Mass., 1991), pp. 291–301.

Cohen, M.

M. Cohen, J. Wallace, Radiosity and Realistic Image Synthesis (Academic Press Professional, Cambridge, Mass., 1993).

Curran, W.

A. Johnston, W. Curran, “Investigating shape-from-shading illusions using solid objects,” Vision Res. 36, 2827–2835 (1996).
[CrossRef] [PubMed]

D’Zmura, M.

M. D’Zmura, “Shading ambiguity: reflectance and illumination,” in Computational Models of Visual Processing, M. S. Landy, J. A. Movshon, eds. (MIT Press, Cambridge, Mass., 1991), pp. 187–208.

Feiner, S. K.

J. D. Foley, A. van Dam, S. K. Feiner, J. F. Hughes, Computer Graphics: Principles and Practice (Addison-Wesley, Reading, Mass., 1990).

Foley, J. D.

J. D. Foley, A. van Dam, S. K. Feiner, J. F. Hughes, Computer Graphics: Principles and Practice (Addison-Wesley, Reading, Mass., 1990).

Gilchrist, A.

A. Gilchrist, A. Jacobsen, “Perception of lightness and illumination in a world of one reflectance,” Perception 13, 5–19 (1984).
[PubMed]

Gilchrist, A. L.

A. L. Gilchrist, “When does perceived lightness depend on perceived spatial arrangement?” Percept. Psychophys. 28, 527–538 (1980).
[CrossRef] [PubMed]

A. L. Gilchrist, “The perception of surface blacks and whites,” Sci. Am. 240, 112–124 (1979).
[CrossRef] [PubMed]

A. L. Gilchrist, “Perceived lightness depends on perceived spatial arrangement,” Science 195, 185–187 (1977).
[CrossRef] [PubMed]

Hall, E.

E. Hall, Computer Image Processing and Recognition (Academic, New York, 1979).

Hughes, J. F.

J. D. Foley, A. van Dam, S. K. Feiner, J. F. Hughes, Computer Graphics: Principles and Practice (Addison-Wesley, Reading, Mass., 1990).

Hurlbert, A. C.

D. Kersten, A. C. Hurlbert, “Discounting the color of mutual illumination: a 3D-shape-induced color phenomenon,” Invest. Ophthalmol. Visual Sci. 37, S1065 (1996).

Jacobsen, A.

A. Gilchrist, A. Jacobsen, “Perception of lightness and illumination in a world of one reflectance,” Perception 13, 5–19 (1984).
[PubMed]

Johnston, A.

A. Johnston, W. Curran, “Investigating shape-from-shading illusions using solid objects,” Vision Res. 36, 2827–2835 (1996).
[CrossRef] [PubMed]

A. Johnston, P. J. Passmore, “Independent encoding of surface orientation and surface curvature,” Vision Res. 34, 3005–3012 (1994).
[CrossRef] [PubMed]

Kawato, M.

F. E. Pollick, S. Nishida, Y. Koike, M. Kawato, “Perceived motion in structure from motion: pointing responses to the axis of rotation,” Percept. Psychophys. 56, 91–109 (1994).
[CrossRef] [PubMed]

Kersten, D.

D. Kersten, A. C. Hurlbert, “Discounting the color of mutual illumination: a 3D-shape-induced color phenomenon,” Invest. Ophthalmol. Visual Sci. 37, S1065 (1996).

D. C. Knill, D. Kersten, “Apparent surface curvature affects lightness perception,” Nature (London) 351, 228–230 (1991).
[CrossRef]

Knill, D. C.

D. C. Knill, D. Kersten, “Apparent surface curvature affects lightness perception,” Nature (London) 351, 228–230 (1991).
[CrossRef]

Koike, Y.

F. E. Pollick, S. Nishida, Y. Koike, M. Kawato, “Perceived motion in structure from motion: pointing responses to the axis of rotation,” Percept. Psychophys. 56, 91–109 (1994).
[CrossRef] [PubMed]

Land, E. H.

E. H. Land, “Recent advances in retinex theory,” Vision Res. 26, 7–22 (1986).
[CrossRef] [PubMed]

Landy, M.

C. Chubb, M. Landy, “Orthogonal distribution analysis: a new approach to the study of texture perception,” in Computational Models of Visual Processing, M. S. Landy, J. A. Movshon, eds. (MIT Press, Cambridge, Mass., 1991), pp. 291–301.

Nishida, S.

F. E. Pollick, S. Nishida, Y. Koike, M. Kawato, “Perceived motion in structure from motion: pointing responses to the axis of rotation,” Percept. Psychophys. 56, 91–109 (1994).
[CrossRef] [PubMed]

Passmore, P. J.

A. Johnston, P. J. Passmore, “Independent encoding of surface orientation and surface curvature,” Vision Res. 34, 3005–3012 (1994).
[CrossRef] [PubMed]

Phong, B. T.

B. T. Phong, “Illumination for computer generated pictures,” Commun. ACM 18, 311–317 (1975).
[CrossRef]

Pollick, F. E.

F. E. Pollick, S. Nishida, Y. Koike, M. Kawato, “Perceived motion in structure from motion: pointing responses to the axis of rotation,” Percept. Psychophys. 56, 91–109 (1994).
[CrossRef] [PubMed]

Reichel, F. D.

J. T. Todd, F. D. Reichel, “Ordinal structure in the vi-sual perception and cognition of smoothly curved surfaces,” Psychol. Rev. 96, 643–657 (1989).
[CrossRef] [PubMed]

Todd, J. T.

J. T. Todd, P. Bressan, “The perception of 3-dimensional affine structure from minimal apparent motion sequences,” Percept. Psychophys. 48, 419–430 (1990).
[CrossRef] [PubMed]

J. T. Todd, F. D. Reichel, “Ordinal structure in the vi-sual perception and cognition of smoothly curved surfaces,” Psychol. Rev. 96, 643–657 (1989).
[CrossRef] [PubMed]

van Dam, A.

J. D. Foley, A. van Dam, S. K. Feiner, J. F. Hughes, Computer Graphics: Principles and Practice (Addison-Wesley, Reading, Mass., 1990).

Wallace, J.

M. Cohen, J. Wallace, Radiosity and Realistic Image Synthesis (Academic Press Professional, Cambridge, Mass., 1993).

Wandell, B. A.

Commun. ACM

B. T. Phong, “Illumination for computer generated pictures,” Commun. ACM 18, 311–317 (1975).
[CrossRef]

Invest. Ophthalmol. Visual Sci.

D. Kersten, A. C. Hurlbert, “Discounting the color of mutual illumination: a 3D-shape-induced color phenomenon,” Invest. Ophthalmol. Visual Sci. 37, S1065 (1996).

J. Opt. Soc. Am. A

Nature (London)

D. C. Knill, D. Kersten, “Apparent surface curvature affects lightness perception,” Nature (London) 351, 228–230 (1991).
[CrossRef]

A. Blake, H. Bülthoff, “Does the brain know the physics of specular reflection?” Nature (London) 343, 165–168 (1990).
[CrossRef]

Percept. Psychophys.

A. L. Gilchrist, “When does perceived lightness depend on perceived spatial arrangement?” Percept. Psychophys. 28, 527–538 (1980).
[CrossRef] [PubMed]

J. T. Todd, P. Bressan, “The perception of 3-dimensional affine structure from minimal apparent motion sequences,” Percept. Psychophys. 48, 419–430 (1990).
[CrossRef] [PubMed]

F. E. Pollick, S. Nishida, Y. Koike, M. Kawato, “Perceived motion in structure from motion: pointing responses to the axis of rotation,” Percept. Psychophys. 56, 91–109 (1994).
[CrossRef] [PubMed]

Perception

A. Gilchrist, A. Jacobsen, “Perception of lightness and illumination in a world of one reflectance,” Perception 13, 5–19 (1984).
[PubMed]

Psychol. Rev.

J. T. Todd, F. D. Reichel, “Ordinal structure in the vi-sual perception and cognition of smoothly curved surfaces,” Psychol. Rev. 96, 643–657 (1989).
[CrossRef] [PubMed]

Sci. Am.

A. L. Gilchrist, “The perception of surface blacks and whites,” Sci. Am. 240, 112–124 (1979).
[CrossRef] [PubMed]

Science

E. H. Adelson, “Perceptual organization and the judgement of brightness,” Science 262, 2042–2044 (1993).
[CrossRef] [PubMed]

A. L. Gilchrist, “Perceived lightness depends on perceived spatial arrangement,” Science 195, 185–187 (1977).
[CrossRef] [PubMed]

Vision Res.

A. Johnston, P. J. Passmore, “Independent encoding of surface orientation and surface curvature,” Vision Res. 34, 3005–3012 (1994).
[CrossRef] [PubMed]

E. H. Land, “Recent advances in retinex theory,” Vision Res. 26, 7–22 (1986).
[CrossRef] [PubMed]

A. Johnston, W. Curran, “Investigating shape-from-shading illusions using solid objects,” Vision Res. 36, 2827–2835 (1996).
[CrossRef] [PubMed]

Other

J. D. Foley, A. van Dam, S. K. Feiner, J. F. Hughes, Computer Graphics: Principles and Practice (Addison-Wesley, Reading, Mass., 1990).

E. Hall, Computer Image Processing and Recognition (Academic, New York, 1979).

C. Chubb, M. Landy, “Orthogonal distribution analysis: a new approach to the study of texture perception,” in Computational Models of Visual Processing, M. S. Landy, J. A. Movshon, eds. (MIT Press, Cambridge, Mass., 1991), pp. 291–301.

M. D’Zmura, “Shading ambiguity: reflectance and illumination,” in Computational Models of Visual Processing, M. S. Landy, J. A. Movshon, eds. (MIT Press, Cambridge, Mass., 1991), pp. 187–208.

M. Cohen, J. Wallace, Radiosity and Realistic Image Synthesis (Academic Press Professional, Cambridge, Mass., 1993).

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Figures (13)

Fig. 1
Fig. 1

Stimulus display. The center window on the left side of the display is the matching window, in which a target surface (left) and a match surface (right) are presented. The large window on the right is the reference window. Subjects could change the reflectance of the match surface by clicking one of the boxes in the reference window.

Fig. 2
Fig. 2

Smooth surfaces defined by the seven shape parameters used for the target surfaces in experiment 1. The spatial-frequency parameter s is 1.5, 3.0, and 6.0 for the top, center, and bottom rows, respectively, and the amplitude parameter a is 0.1, 0.2, and 0.4 for the left, center and right columns, respectively. The reflectance parameters are kd=0.3, n=10.

Fig. 3
Fig. 3

Smooth surfaces defined by the nine reflectance parameters used for the target surfaces in experiment 1. The diffuse reflectance (kd) is 0.2, 0.3, and 0.4 for the left, center, and right columns, respectively, and the shininess (n) was 3, 10, and 29 for the bottom, center, and top rows, respectively. The shape parameters are s=3.0, a=0.2.

Fig. 4
Fig. 4

Average results of the reflectance-matching task for five subjects with use of surfaces with smoothly modulated depth and Phong reflectances (experiment 1). The results obtained with different target-shape parameters are shown separately in the seven panels, which are arranged in the same way as the surface images in Fig. 2. The modulation spatial frequency increases from top to bottom, and the modulation amplitude increases from left to right. For each panel the horizontal axis is the diffuse reflectance and the vertical axis is the logarithm of the shininess. The small circles indicate the reflectance parameters of the target. The position of each circle coincides with the position of the corresponding surface in Fig. 3. The mean position (center of gravity) of the subjects’ responses is indicated by an arrow coming out of each small circle. Thus the arrow indicates the direction and size of the constant error. An ellipsoid around the end point of the arrow indicates the standard deviation, which was calculated from the covariance matrix of the response. (a) Viewer-rotation condition, (b) surface-rotation condition.

Fig. 5
Fig. 5

Average results for the reflectance-matching task for five subjects with use of surfaces with smoothly modulated depth and Lambertian reflectances (experiment 2). The results obtained with different target-shape parameters are shown separately in the seven panels, as in Fig. 4. For each panel the horizontal axis is simulated diffuse reflectance and the vertical axis is the matching value. Error bars indicate the standard deviation, and the dotted diagonal line indicates the perfect match. (a) Viewer-rotation condition, (b) surface-rotation condition.

Fig. 6
Fig. 6

Polygonal surfaces of three shape parameters used for the target in experiment 3. The spatial-frequency parameter s is 3.0, and the amplitude parameter a is 0.1, 0.2 and 0.4 for the left, center, and right surfaces, respectively. The reflectance parameters are kd=0.3, n=10.

Fig. 7
Fig. 7

Average results for the reflectance-matching task for five subjects with use of polygonal surfaces and Phong reflectances (experiment 3a). The results obtained with different target shape parameters are shown separately in three panels for each rotation condition, arranged in the same way as surface images in Fig. 6. See the caption of Fig. 4 for other details.

Fig. 8
Fig. 8

Average results for the reflectance-matching task for five subjects with use of polygonal target surfaces and smooth match surfaces (experiment 3b), for two rotation conditions.

Fig. 9
Fig. 9

Results of experiment 1 predicted by the luminance-histogram matching model, shown in the same format as in Fig. 4. See text for further details.

Fig. 10
Fig. 10

Distributions of luminance-histogram distances of targets from the match stimuli chosen by the subjects (solid curves), and distributions of distances of targets from the match stimuli that have the same reflectance parameters (dotted curves) based on the results of experiment 1. The abscissa was normalized by the minimum histogram distance, and the ordinate was normalized by the peak of each curve.

Fig. 11
Fig. 11

Results of experiment 2 predicted by the luminance-histogram matching model, shown in the same format as in Fig. 5.

Fig. 12
Fig. 12

Example of a luminance-histogram equation. Left, a smooth surface rendered with shape parameters s=3.0 and a=0.2 and reflectance parameters kd=0.3 and n=10. Center, a surface rendered with the same parameters as the left one, except for the larger modulation amplitude (a=0.4). Right, a surface with the same shape as the left surface, with the luminance histogram equated to that of the center surface. Apparent surface reflectance of the center surface is more similar to that of the right surface than to that of the left surface.

Fig. 13
Fig. 13

Luminance-histogram equation factor for a match surface [shape parameters (3.0, 0.2)] selected by the subjects (ordinate) to match the apparent surface reflectance with target surfaces of various shapes (abscissa). Symbols indicate different target-surface reflectances (kd, n). Dotted curves, average of the Lambertian surfaces; solid curves, average of the Phong surfaces. Each point is based on five judgments.

Tables (1)

Tables Icon

Table 1 Results of Student’s T-Test for Experiments 1–3a

Equations (4)

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

HA(fx, fy)=a exp[-(fx2+fy2)/s2],
I=Iaka+Ipkd  cos(θ)+Ipks  cosn(α),
D12=F=1FmaxI=1Imax|N1,F(I)-N2,F(I)|m,
I(x, y)=(1-aeq)I0(x, y)+aeqI1(x, y),

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