Abstract

The task of distinguishing material changes from shadow boundaries in chromatic images is discussed. Although there have been previous attempts at providing solutions to this problem, the assumptions that were adopted were too restrictive. Using a simple reflection model, we show that the ambient illumination cannot be assumed to have the same spectral characteristics as the incident illumination, since it may lead to the classification of shadow boundaries as material changes. In such cases, we show that it is necessary to take into account the spectral properties of the ambient illumination in order to develop a technique that is more robust and stable than previous techniques. This technique uses a biologically motivated model of color vision and, in particular, a set of chromatic-opponent and double-opponent center-surround operators. We apply this technique to simulated test patterns as well as to a chromatic image. It is shown that, given some knowledge about the strength of the ambient illumination, this method provides a better classification of shadow boundaries and material changes.

© 1986 Optical Society of America

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References

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  1. R. Nevatia, “A color edge detector and its use in scene segmentation,” IEEE Trans. Syst. Man Cybern. SMC-7, 820–826 (1977).
  2. G. B. Coleman, H. C. Andrews, “Imaging segmentation by clustering,” Proc. IEEE 67, 773–785 (1979).
    [Crossref]
  3. P. A. Nagin, A. R. Hanson, E. M. Riseman, “Studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 263–276 (1982).
    [Crossref]
  4. B. J. Schacter, L. S. Davis, A. Rosenfeld, “Scene segmentation by cluster detection in color spaces,” SIGART Newsl. 58, 16–17 (1976).
  5. R. Ohlander, K. E. Price, D. R. Reddy, “Picture segmentation using a recursive region splitting method,” Comput. Graphics Image Process. 8, 313–333 (1978).
    [Crossref]
  6. M. D. Levine, S. I. Shaheen, “A modular computer vision system for picture segmentation and interpretation, Part 1,” in Proceedings of IEEE Conference on Pattern Recognition and Image Processing (Institute of Electrical and Electronics Engineers, New York, 1979), pp. 523–533.
  7. M. Nagao, T. Matsuyama, Y. Ikeda, “Region extraction and shape analysis in aerial photographs,” Comput. Graphics Image Process. 10, 195–223 (1979).
    [Crossref]
  8. K. E. Sloan, “World model driven recognition of natural scenes,” Ph.D. dissertation (University of Pennsylvania, Philadelphia, Pa., 1977).
  9. J. M. Rubin, W. A. Richards, “Color vision and image intensities: when are changes material?” Biol. Cybern. 45, 215–226 (1982).
    [Crossref] [PubMed]
  10. J. M. Rubin, W. A. Richards, “Color vision: representing material changes,” AI Memo 764 (MIT Artificial Intelligence Laboratory, Cambridge, Mass., 1984).
  11. S. A. Shafer, “Using color to separate reflection components,” Technical Rep. TR-136 (Department of Computer Science, University of Rochester, Rochester, N.Y., 1984).
  12. R. L. Cook, K. E. Torrance, “A reflectance model for computer graphics,” Comput. Graphics 15, 307–316 (1981).
    [Crossref]
  13. R. M. Boynton, D. N. Whitten, “Visual adaptation in monkey cones: recordings of late receptor potentials,” Science 170, 1423–1426 (1970).
    [Crossref] [PubMed]
  14. W. A. H. Rushton, “Peripheral coding in the nervous system,” in Sensory Communication, W. A. Rosenblith, ed. (MIT Press, Cambridge, Mass., 1961).
  15. M. S. Livingstone, D. H. Hubel, “Anatomy and physiology of a color system in the primate visual cortex,” J. Neurosci. 4, 309–356 (1984).
    [PubMed]
  16. C. R. Michael, “Color vision mechanisms in monkey striate cortex: dual-opponent cells with concentric receptive fields,” J. Neurophys. 41, 557–576 (1978).
  17. R. Gershon, “Empirical results with a model of color vision,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 1985), pp. 302–305.
  18. M. S. Longuet-Higgins, “The distribution of intervals between zeros of a stationary random function,” Phil. Trans. R. Soc. London Ser. A 254, 557–599 (1962).
    [Crossref]
  19. D. J. Fleet, A. D. Jepson, P. E. Hallett, “A spatio-temporal model for early visual processing,” Technical Rep. RBCV-TR-84-1 (Department of Computer Science, University of Toronto, Toronto, 1984).

1984 (1)

M. S. Livingstone, D. H. Hubel, “Anatomy and physiology of a color system in the primate visual cortex,” J. Neurosci. 4, 309–356 (1984).
[PubMed]

1982 (2)

P. A. Nagin, A. R. Hanson, E. M. Riseman, “Studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 263–276 (1982).
[Crossref]

J. M. Rubin, W. A. Richards, “Color vision and image intensities: when are changes material?” Biol. Cybern. 45, 215–226 (1982).
[Crossref] [PubMed]

1981 (1)

R. L. Cook, K. E. Torrance, “A reflectance model for computer graphics,” Comput. Graphics 15, 307–316 (1981).
[Crossref]

1979 (2)

M. Nagao, T. Matsuyama, Y. Ikeda, “Region extraction and shape analysis in aerial photographs,” Comput. Graphics Image Process. 10, 195–223 (1979).
[Crossref]

G. B. Coleman, H. C. Andrews, “Imaging segmentation by clustering,” Proc. IEEE 67, 773–785 (1979).
[Crossref]

1978 (2)

R. Ohlander, K. E. Price, D. R. Reddy, “Picture segmentation using a recursive region splitting method,” Comput. Graphics Image Process. 8, 313–333 (1978).
[Crossref]

C. R. Michael, “Color vision mechanisms in monkey striate cortex: dual-opponent cells with concentric receptive fields,” J. Neurophys. 41, 557–576 (1978).

1977 (1)

R. Nevatia, “A color edge detector and its use in scene segmentation,” IEEE Trans. Syst. Man Cybern. SMC-7, 820–826 (1977).

1976 (1)

B. J. Schacter, L. S. Davis, A. Rosenfeld, “Scene segmentation by cluster detection in color spaces,” SIGART Newsl. 58, 16–17 (1976).

1970 (1)

R. M. Boynton, D. N. Whitten, “Visual adaptation in monkey cones: recordings of late receptor potentials,” Science 170, 1423–1426 (1970).
[Crossref] [PubMed]

1962 (1)

M. S. Longuet-Higgins, “The distribution of intervals between zeros of a stationary random function,” Phil. Trans. R. Soc. London Ser. A 254, 557–599 (1962).
[Crossref]

Andrews, H. C.

G. B. Coleman, H. C. Andrews, “Imaging segmentation by clustering,” Proc. IEEE 67, 773–785 (1979).
[Crossref]

Boynton, R. M.

R. M. Boynton, D. N. Whitten, “Visual adaptation in monkey cones: recordings of late receptor potentials,” Science 170, 1423–1426 (1970).
[Crossref] [PubMed]

Coleman, G. B.

G. B. Coleman, H. C. Andrews, “Imaging segmentation by clustering,” Proc. IEEE 67, 773–785 (1979).
[Crossref]

Cook, R. L.

R. L. Cook, K. E. Torrance, “A reflectance model for computer graphics,” Comput. Graphics 15, 307–316 (1981).
[Crossref]

Davis, L. S.

B. J. Schacter, L. S. Davis, A. Rosenfeld, “Scene segmentation by cluster detection in color spaces,” SIGART Newsl. 58, 16–17 (1976).

Fleet, D. J.

D. J. Fleet, A. D. Jepson, P. E. Hallett, “A spatio-temporal model for early visual processing,” Technical Rep. RBCV-TR-84-1 (Department of Computer Science, University of Toronto, Toronto, 1984).

Gershon, R.

R. Gershon, “Empirical results with a model of color vision,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 1985), pp. 302–305.

Hallett, P. E.

D. J. Fleet, A. D. Jepson, P. E. Hallett, “A spatio-temporal model for early visual processing,” Technical Rep. RBCV-TR-84-1 (Department of Computer Science, University of Toronto, Toronto, 1984).

Hanson, A. R.

P. A. Nagin, A. R. Hanson, E. M. Riseman, “Studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 263–276 (1982).
[Crossref]

Hubel, D. H.

M. S. Livingstone, D. H. Hubel, “Anatomy and physiology of a color system in the primate visual cortex,” J. Neurosci. 4, 309–356 (1984).
[PubMed]

Ikeda, Y.

M. Nagao, T. Matsuyama, Y. Ikeda, “Region extraction and shape analysis in aerial photographs,” Comput. Graphics Image Process. 10, 195–223 (1979).
[Crossref]

Jepson, A. D.

D. J. Fleet, A. D. Jepson, P. E. Hallett, “A spatio-temporal model for early visual processing,” Technical Rep. RBCV-TR-84-1 (Department of Computer Science, University of Toronto, Toronto, 1984).

Levine, M. D.

M. D. Levine, S. I. Shaheen, “A modular computer vision system for picture segmentation and interpretation, Part 1,” in Proceedings of IEEE Conference on Pattern Recognition and Image Processing (Institute of Electrical and Electronics Engineers, New York, 1979), pp. 523–533.

Livingstone, M. S.

M. S. Livingstone, D. H. Hubel, “Anatomy and physiology of a color system in the primate visual cortex,” J. Neurosci. 4, 309–356 (1984).
[PubMed]

Longuet-Higgins, M. S.

M. S. Longuet-Higgins, “The distribution of intervals between zeros of a stationary random function,” Phil. Trans. R. Soc. London Ser. A 254, 557–599 (1962).
[Crossref]

Matsuyama, T.

M. Nagao, T. Matsuyama, Y. Ikeda, “Region extraction and shape analysis in aerial photographs,” Comput. Graphics Image Process. 10, 195–223 (1979).
[Crossref]

Michael, C. R.

C. R. Michael, “Color vision mechanisms in monkey striate cortex: dual-opponent cells with concentric receptive fields,” J. Neurophys. 41, 557–576 (1978).

Nagao, M.

M. Nagao, T. Matsuyama, Y. Ikeda, “Region extraction and shape analysis in aerial photographs,” Comput. Graphics Image Process. 10, 195–223 (1979).
[Crossref]

Nagin, P. A.

P. A. Nagin, A. R. Hanson, E. M. Riseman, “Studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 263–276 (1982).
[Crossref]

Nevatia, R.

R. Nevatia, “A color edge detector and its use in scene segmentation,” IEEE Trans. Syst. Man Cybern. SMC-7, 820–826 (1977).

Ohlander, R.

R. Ohlander, K. E. Price, D. R. Reddy, “Picture segmentation using a recursive region splitting method,” Comput. Graphics Image Process. 8, 313–333 (1978).
[Crossref]

Price, K. E.

R. Ohlander, K. E. Price, D. R. Reddy, “Picture segmentation using a recursive region splitting method,” Comput. Graphics Image Process. 8, 313–333 (1978).
[Crossref]

Reddy, D. R.

R. Ohlander, K. E. Price, D. R. Reddy, “Picture segmentation using a recursive region splitting method,” Comput. Graphics Image Process. 8, 313–333 (1978).
[Crossref]

Richards, W. A.

J. M. Rubin, W. A. Richards, “Color vision and image intensities: when are changes material?” Biol. Cybern. 45, 215–226 (1982).
[Crossref] [PubMed]

J. M. Rubin, W. A. Richards, “Color vision: representing material changes,” AI Memo 764 (MIT Artificial Intelligence Laboratory, Cambridge, Mass., 1984).

Riseman, E. M.

P. A. Nagin, A. R. Hanson, E. M. Riseman, “Studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 263–276 (1982).
[Crossref]

Rosenfeld, A.

B. J. Schacter, L. S. Davis, A. Rosenfeld, “Scene segmentation by cluster detection in color spaces,” SIGART Newsl. 58, 16–17 (1976).

Rubin, J. M.

J. M. Rubin, W. A. Richards, “Color vision and image intensities: when are changes material?” Biol. Cybern. 45, 215–226 (1982).
[Crossref] [PubMed]

J. M. Rubin, W. A. Richards, “Color vision: representing material changes,” AI Memo 764 (MIT Artificial Intelligence Laboratory, Cambridge, Mass., 1984).

Rushton, W. A. H.

W. A. H. Rushton, “Peripheral coding in the nervous system,” in Sensory Communication, W. A. Rosenblith, ed. (MIT Press, Cambridge, Mass., 1961).

Schacter, B. J.

B. J. Schacter, L. S. Davis, A. Rosenfeld, “Scene segmentation by cluster detection in color spaces,” SIGART Newsl. 58, 16–17 (1976).

Shafer, S. A.

S. A. Shafer, “Using color to separate reflection components,” Technical Rep. TR-136 (Department of Computer Science, University of Rochester, Rochester, N.Y., 1984).

Shaheen, S. I.

M. D. Levine, S. I. Shaheen, “A modular computer vision system for picture segmentation and interpretation, Part 1,” in Proceedings of IEEE Conference on Pattern Recognition and Image Processing (Institute of Electrical and Electronics Engineers, New York, 1979), pp. 523–533.

Sloan, K. E.

K. E. Sloan, “World model driven recognition of natural scenes,” Ph.D. dissertation (University of Pennsylvania, Philadelphia, Pa., 1977).

Torrance, K. E.

R. L. Cook, K. E. Torrance, “A reflectance model for computer graphics,” Comput. Graphics 15, 307–316 (1981).
[Crossref]

Whitten, D. N.

R. M. Boynton, D. N. Whitten, “Visual adaptation in monkey cones: recordings of late receptor potentials,” Science 170, 1423–1426 (1970).
[Crossref] [PubMed]

Biol. Cybern. (1)

J. M. Rubin, W. A. Richards, “Color vision and image intensities: when are changes material?” Biol. Cybern. 45, 215–226 (1982).
[Crossref] [PubMed]

Comput. Graphics (1)

R. L. Cook, K. E. Torrance, “A reflectance model for computer graphics,” Comput. Graphics 15, 307–316 (1981).
[Crossref]

Comput. Graphics Image Process. (2)

R. Ohlander, K. E. Price, D. R. Reddy, “Picture segmentation using a recursive region splitting method,” Comput. Graphics Image Process. 8, 313–333 (1978).
[Crossref]

M. Nagao, T. Matsuyama, Y. Ikeda, “Region extraction and shape analysis in aerial photographs,” Comput. Graphics Image Process. 10, 195–223 (1979).
[Crossref]

IEEE Trans. Pattern Anal. Machine Intell. (1)

P. A. Nagin, A. R. Hanson, E. M. Riseman, “Studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 263–276 (1982).
[Crossref]

IEEE Trans. Syst. Man Cybern. (1)

R. Nevatia, “A color edge detector and its use in scene segmentation,” IEEE Trans. Syst. Man Cybern. SMC-7, 820–826 (1977).

J. Neurophys. (1)

C. R. Michael, “Color vision mechanisms in monkey striate cortex: dual-opponent cells with concentric receptive fields,” J. Neurophys. 41, 557–576 (1978).

J. Neurosci. (1)

M. S. Livingstone, D. H. Hubel, “Anatomy and physiology of a color system in the primate visual cortex,” J. Neurosci. 4, 309–356 (1984).
[PubMed]

Phil. Trans. R. Soc. London Ser. A (1)

M. S. Longuet-Higgins, “The distribution of intervals between zeros of a stationary random function,” Phil. Trans. R. Soc. London Ser. A 254, 557–599 (1962).
[Crossref]

Proc. IEEE (1)

G. B. Coleman, H. C. Andrews, “Imaging segmentation by clustering,” Proc. IEEE 67, 773–785 (1979).
[Crossref]

Science (1)

R. M. Boynton, D. N. Whitten, “Visual adaptation in monkey cones: recordings of late receptor potentials,” Science 170, 1423–1426 (1970).
[Crossref] [PubMed]

SIGART Newsl. (1)

B. J. Schacter, L. S. Davis, A. Rosenfeld, “Scene segmentation by cluster detection in color spaces,” SIGART Newsl. 58, 16–17 (1976).

Other (7)

K. E. Sloan, “World model driven recognition of natural scenes,” Ph.D. dissertation (University of Pennsylvania, Philadelphia, Pa., 1977).

M. D. Levine, S. I. Shaheen, “A modular computer vision system for picture segmentation and interpretation, Part 1,” in Proceedings of IEEE Conference on Pattern Recognition and Image Processing (Institute of Electrical and Electronics Engineers, New York, 1979), pp. 523–533.

W. A. H. Rushton, “Peripheral coding in the nervous system,” in Sensory Communication, W. A. Rosenblith, ed. (MIT Press, Cambridge, Mass., 1961).

J. M. Rubin, W. A. Richards, “Color vision: representing material changes,” AI Memo 764 (MIT Artificial Intelligence Laboratory, Cambridge, Mass., 1984).

S. A. Shafer, “Using color to separate reflection components,” Technical Rep. TR-136 (Department of Computer Science, University of Rochester, Rochester, N.Y., 1984).

D. J. Fleet, A. D. Jepson, P. E. Hallett, “A spatio-temporal model for early visual processing,” Technical Rep. RBCV-TR-84-1 (Department of Computer Science, University of Toronto, Toronto, 1984).

R. Gershon, “Empirical results with a model of color vision,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Institute of Electrical and Electronics Engineers, New York, 1985), pp. 302–305.

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Figures (5)

Fig. 1
Fig. 1

A case in which the ambient illumination impinging upon an object does not have the same spectral characteristics as the incident illumination.

Fig. 2
Fig. 2

The red and green components of the vectors describing the diffuse reflected intensity of an object, its normal, and the additional ambient reflected intensity.

Fig. 3
Fig. 3

Responses of monochromatic opponent units and double-opponent operators to two sets of test patterns that include a step-edge in space in which the spectral density changes. The red and green components of the test patterns are (150, 200) followed by (90, 100) for plots (a), (c), and (e) and (90, 100) followed by (60, 50) for plots (b), (d), and (f). The additional ambient reflected intensity was assumed to be 30 units of red, and α = 0.5. Plots (a) and (b) are responses of an R+ center/R− surround operator, plots (c) and (d) are of a G+ center/G− surround operator, and plots (e) and (f) are of an R+G center/RG+ surround operator. The radius of the center of all operators used is 7 pixels, and σs/σc is 2.5.

Fig. 4
Fig. 4

Comparisons between the pull factor and the relative amplitude responses of the units (red and green opponent units and R+G− center/RG+ surround operator).

Fig. 5
Fig. 5

An application of the algorithm to a chromatic image, (a) Is the original image, with a red pepper (1) casting its color on a green pepper (2). (b) Is the result of the algorithm run with predicted pull factor of zero; the bright lines are the discontinuities hypothesized as material changes, (c) Is the result of the algorithm with predicted pull factor of 0.2, and (d) with a pull factor of 0.3; the darker lines are discontinuities that were reclassified as shadow boundaries. The images in (b), (c), and (d) are shown with reduced contrast so that the discontinuities will be easier to see.

Tables (2)

Tables Icon

Table 1 The Response of the Red and Green Mechanisms to the Reflected Intensities of the Shadow and Lit Regions Under Ideal and Nonideal Lighting Conditions

Tables Icon

Table 2 An Example Taken from an Image Taken Over a Shadow Boundary That Is Classified as a Material Change by the Opposite Slope Sign Condition

Equations (12)

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

I ref ( λ , x ) = I ambient ( λ , x ) ρ ambient ( λ , x ) + I incident ( λ , x ) × [ δ ρ specular ( λ , x ) + ( 1 δ ) ρ diffuse ( λ , x ) ] ,
I ref ( λ , x ) = I ambient ( λ , x ) ρ ( λ , x ) + I incident ( λ , x ) ρ ( λ , x ) = [ I ambient ( λ , x ) + I incident ( λ , x ) ] ρ ( λ , x ) ,
I ref ( λ , x ) = I ambient ( λ , x ) ρ ( λ , x ) ,
R ( x ) = 0 I ref ( λ , x ) S R ( λ ) d λ ,
( I X λ 1 I Y λ 1 ) ( I X λ 2 I Y λ 2 ) < 0.
( I X λ 2 I X λ 1 ) ( I Y λ 2 I Y λ 1 ) < 0 ,
Amount of pull = ( r , g ) ( R , G ) ,
Pull factor = ( r , g ) ( G , R ) | ( R , G ) | 2 = | g R r G | R 2 + G 2 .
RESP ( x ; σ ) = G ( x ; σ c ) * L R ( x ) G ( x ; σ s ) * L R ( x ) = G ( x r ; σ c ) L R ( r ) d r G ( x r ; σ s ) L R ( r ) d r ,
G ( x ; σ i ) = 1 2 π σ i 2 exp ( | x | 2 2 σ i 2 ) .
RESP ( x ; σ ) = G ( x ; σ c ) * [ L R ( x ) L G ( x ) ] G ( x ; σ s ) * [ L R ( x ) L G ( x ) ] = [ L R ( x ) L G ( x ) ] DOG ( x ) .
Relative Amplitude Response = | Peak Response of R + G / R G + | [ ( Peak Response of R + / R ) 2 + ( Peak Response of G + / G ) 2 ] 1 / 2 .

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