Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/1837101.1837107acmconferencesArticle/Chapter ViewAbstractPublication PagessiggraphConference Proceedingsconference-collections
research-article

Image statistics: from data collection to applications in graphics

Published: 26 July 2010 Publication History

Abstract

Natural images exhibit statistical regularities that differentiate them from random collections of pixels. Moreover, the human visual system appears to have evolved to exploit such statistical regularities. As computer graphics is concerned with producing imagery for observation by humans, it would be prudent to understand which statistical regularities occur in nature, so they can be emulated by image synthesis methods. In this course we introduce all aspects of natural image statistics, ranging from data collection to analysis and finally their applications in computer graphics, computational photography, visualization and image processing.

Supplementary Material

MP4 File (cs016-10.mp4)

References

[1]
Baddeley, R. J., and Hancock, P. J. B. 1991. A statistical analysis of natural images matches psychophysically derived orientation tuning curves. Proc. Roy. Soc. Lond. B 246, 219--223.
[2]
Balboa, R., and Grzywacz, N. 2000. Occlusions and their relationship with the distribution of contrasts in natural images. Vision Research.
[3]
Balboa, R. M., Tyler, C. W., and Grzywacz, N. M. 2001. Occlusions contribute to scaling in natural images. Vision Research 41, 7, 955--964.
[4]
Bell, A. J., and Sejnowski, T. J. 1996. Edges are the 'independent components' of natural scenes. Advances in Neural Information Processing Systems 9.
[5]
Bell, A. J., and Sejnowski, T. J. 1997. The independent components of natural scenes are edge filters. Vision Research 37, 3327--3338.
[6]
Bischof, H., and Leonardis, A. 2000. Recognizing objects by their appearance using eigenimages. LECTURE NOTES IN COMPUTER SCIENCE, 245--265.
[7]
Brady, M., and Legge, G. 2009. Camera calibration for natural image studies and vision research. Journal of the Optical Society of America A 26, 1, 30--42.
[8]
Burton, G. J., and Moorhead, I. R. 1987. Color and spatial structure in natural scenes. Applied Optics 26, 1 (January), 157--170.
[9]
Chiao, C.-C., Cronin, T. W., and Osorio, D. 2000. Color signals in natural scenes: characteristics of reflectance spectra and the effects of natural illuminants. J. Opt. Soc. Am. A 17, 2 (February), 218--224.
[10]
Chiao, C.-C., Osorio, D., Vorobyev, M., and Cronin, T. W. 2000. Characterization of natural illuminants in forests and the use of digital video data to reconstruct illuminant spectra. J. Opt. Soc. Am. A 17, 10 (October), 1713--1721.
[11]
Dong, D. W., and Atick, J. J. 1995. Statistics of natural time-varying images. Network: Computation in Neural Systems 6, 3, 345--358.
[12]
Dong, D. W., and Atick, J. J. 1995. Temporal decorrelation: A theory of lagged and nonlagged responses in the lateral geniculate nucleus. Network: Computation in Neural Systems 6, 2, 159--178.
[13]
Dror, R., Leung, T., Adelson, E., and Willsky, A. 2001. Statistics of real-world illumination. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2.
[14]
Fergus, R., Singh, B., Hertzmann, A., Roweis, S., and Freeman, W. 2006. Removing camera shake from a single photograph. SIGGRAPH '06: SIGGRAPH 2006 Papers.
[15]
Field, D. J., and Brady, N. 1997. Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes. Vision Research 37, 23, 3367--3383.
[16]
Field, D. J. 1987. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4, 12, 2379--2394.
[17]
Field, D. J. 1993. Scale-invariance and self-similar 'wavelet' transforms: An analysis of natural scenes and mammalian visual systems. In Wavelets, fractals and Fourier transforms, M. Farge, J. C. R. Hunt, and J. C. Vassilicos, Eds. Clarendon Press, Oxford, 151--193.
[18]
Field, D. 1999. Wavelets, vision and the statistics of natural scenes. Philosophical Transactions: Mathematical.
[19]
Fleet, D. J., and Jepson, A. D. 1993. Stability of phase information. IEEE Trans. on PAMI 15, 12, 1253--1268.
[20]
Hancock, P. J. B., Baddeley, R. J., and Smith, L. S. 1992. The principle components of natural images. Network 3, 61--70.
[21]
Healey, G., and Kondepudy, R. 1994. Radiometric ccd camera calibration and noise estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 3, 267--276.
[22]
Huang, J., and Mumford, D. 1999. Statistics of natural images and models. In Proc. CVPR 99, 541--547.
[23]
Huang, J., Lee, A., and Mumford, D. 2000. Statistics of range images. Computer Vision and Pattern Recognition.
[24]
Hurri, J., Hyvärinen, A., and Oja, E. 1997. Wavelets and natural image statistics. In Proc. of 10th Scandinavian Conference on Image Analysis, 13--18.
[25]
Kim, S., and Pollefeys, M. 2004. Radiometric alignment of image sequences. Computer Vision and Pattern Recognition.
[26]
Kindlmann, G., and Durkin, J. W. 1998. Semi-automatic generation of transfer functions for direct volume rendering. In VVS '98: Proceedings of the 1998 IEEE symposium on Volume visualization, ACM, New York, NY, USA, 79--86.
[27]
Levin, A., and Weiss, Y. 2007. User assisted separation of reflections from a single image using a sparsity prior. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28]
Levin, A. 2007. Blind motion deblurring using image statistics. Advances in Neural Information Processing Systems.
[29]
Mann, S., and Picard, R. 1995. On being 'undigital' with digital cameras: Extending dynamic range by combining differently exposed pictures. Proceedings of IS&T 48th annual conference, 422--428.
[30]
Mitsunaga, T., and Nayar, S. 1999. Radiometric self calibration. Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. 1.
[31]
van Hateren, H., and van der Schaaf, A. 1998. Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Soc. Lond. B 265, 359--366.
[32]
van Hateren, H., and Ruderman, D. L. 1998. Independent component analysis of natural image sequences yields spatiotemporal filters similar to simple cells in primary visual cortex. Proc. R. Soc. Lond. B 265, 2315--2320.
[33]
Olshausen, B., and Field, D. 1996. Wavelet-like receptive fields emerge from a network that learns sparse codes for natural images. Nature.
[34]
Olshausen, B., and Field, D. 1996. Natural image statistics and efficient coding. Network: Computation in Neural Systems, 7: 333--339.
[35]
Ostiak, P. Implementation of hdr panorama stitching algorithm.
[36]
Párraga, C. A., Brelstaff, G., Troscianko, T., and Moorehead, I. R. 1998. Color and luminance information in natural scenes. J. Opt. Soc. Am. A 15, 3, 563--569.
[37]
Pass, G., and Zabih, R. 1999. Comparing images using joint histograms. Multimedia Systems 7, 3, 234--240.
[38]
Portilla, J., and Simoncelli, E. P. 2000. Image denoising via adjustment of wavelet coefficient magnitude correlation. In Proc. 7th IEEE Int'l Conf. on Image Processing.
[39]
Portilla, J., and Simoncelli, E. P. 2000. A parametric texture model based on joint statistics of complex wavelet coefficients. Int'l Journal of Computer Vision 40, 1 (December), 49--71.
[40]
Pouli, T., Cunningham, D., and Reinhard, E. 2010. Image statistics and their applications in computer graphics. In Eurographics State of the Art Report (STAR).
[41]
Preim, B., and Bartz, D. 2007. Visualization in Medicine: Theory, Algorithms, and Applications (The Morgan Kaufmann Series in Computer Graphics). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
[42]
Rainville, S. J. M., and Kingdom, F. A. A. 1999. Spatial-scale contribution to the detection of mirror symmetry in fractal noise. J. Opt. Soc. Am. A 16, 9 (September), 2112--2123.
[43]
Reinagel, P., and Zadow, A. M. 1999. Natural scene statistics at the centre of gaze. Network: Comput. Neural Syst. 10, 1--10.
[44]
Reinhard, E., Ashikhmin, M., Gooch, B., and Shirley, P. 2001. Color transfer between images. IEEE Computer Graphics and Applications 21 (September/October), 34--41.
[45]
Reinhard, E., Shirley, P., and Troscianko, T. 2001. Natural image statistics for computer graphics. Tech. Rep. UUCS-01-002.
[46]
Reinhard, E., Shirley, P., Ashikhmin, M., and Troscianko, T. 2004. Second order image statistics in computer graphics. Proceedings of the 1st Symposium on Applied perception in graphics and visualization, 99--106.
[47]
Ruderman, D., and Bialek, W. 1994. Statistics of natural images: Scaling in the woods. Physical Review Letters.
[48]
Ruderman, D. L., and Bialek, W. 1994. Statistics of natural images: Scaling in the woods. Physical Review Letters 73, 6, 814--817.
[49]
Ruderman, D. L., Cronin, T. W., and Chiao, C. 1998. Statistics of cone responses to natural images: Implications for visual coding. Journal of the Optical Society of America A 15, 8, 2036--2045.
[50]
Ruderman, D. L. 1994. The statistics of natural images. Network: Computation in Neural Systems, 5, 517--548.
[51]
Ruderman, D. L. 1997. Origins of scaling in natural images. Vision Research 37, 3385--3398.
[52]
Ruderman, D. L. 1997. The statistics of natural images. Network: Computation in Neural Systems 5, 4, 517--548.
[53]
Van Der Schaaf, A. 1998. Natural image statistics and visual processing. PhD thesis, Rijksuniversiteit Groningen, The Netherlands.
[54]
Shah, S., and Aggarwal, J. 1996. Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation*. Pattern Recognition 29, 11, 1775--1788.
[55]
Shan, Q., Jia, J., and Agarwala, A. 2008. High-quality motion deblurring from a single image. SIGGRAPH '08: ACM SIGGRAPH 2008 papers.
[56]
Shih, S., Hung, Y., and Lin, W. 1995. When should we consider lens distortion in camera calibration. Pattern Recognition 28, 3, 447--461.
[57]
Simoncelli, E. P., and Portilla, J. 1998. Texture characterization via joint statistics of wavelet coefficient magnitudes. In Proc. 5th Int'l Conf. on Image Processing.
[58]
Simoncelli, E. P. 1997. Statistical models for images: Compression, restoration and synthesis. In 31st Asilomar Conference on Signals, Systems and Computers.
[59]
Simoncelli, E. P. 1999. Bayesian denoising of visual images in the wavelet domain. In Bayesian Inference in Wavelet Based Models, Springer-Verlag, New York, P. Müller and B. Vidakovic, Eds., vol. 141 of Lecture Notes in Statistics, 291--308.
[60]
Simoncelli, E. P. 1999. Modelling the joint statistics of images in the wavelet domain. In Proc. SPIE 44th Anual Meeting, vol. 3813, 188--195.
[61]
Thomson, M. G. A. 1999. Higher-order structure in natural scenes. Journal of the Optical Society of America A 16, 7, 1549--1553.
[62]
Thomson, M. G. A. 1999. Visual coding and the phase structure of natural scenes. Network: Computation in Neural Systems 10, 2, 123--132.
[63]
Thomson, M. G. A. 2001. Beats, kurtosis and visual coding. Network: Computation in Neural Systems.
[64]
Tolhurst, D. J., Tadmor, Y., and Chiao, T. 1992. Amplitude spectra of natural images. Ophthalmic and Physiological Optics 12, 229--232.
[65]
Tolhurt, D. J., and Tadmor, Y. 1997. Discrimination of changes in the slopes of the amplitude spectra of natural images: Band-limited contrast and psychometric functions. Perception 26, 8, 1011--1025.
[66]
Torralba, A., and Oliva, A. 2003. Statistics of natural image categories. Network: Computation in Neural Systems 14, 391--412.
[67]
van der Schaaf, A., and Hateren, J. V. 1996. Modelling the power spectra of natural images: statistics and information. Vision Research 36, 17, 2759--2770.
[68]
von der Twer, T., and Macleod, D. 2001. Optimal nonlinear codes for the perception of natural colours. Network: Computation in Neural Systems.
[69]
Wainwright, M. J., Simoncelli, E. P., and Willsky, A. S. 2000. Random cascades of gaussian scale mixtures and their use in modelling natural images with application to denoising. In Proc. 7th IEEE Int'l Conf. on Image Processing.
[70]
Webster, M. A., and Miyahara, E. 1997. Contrast adaptation and the spatial structure of natural images. Journal of the Optical Society of America A 14, 9 (September), 2355--2366.
[71]
Willmore, B., and Tolhurst, D. 2001. Characterizing the sparseness of neural codes. Network: Computation in Neural Systems.
[72]
Willson, R. 1994. Modeling and calibration of automated zoom lenses.
[73]
Zetzsche, C., and Rhrbein, F. 2001. Nonlinear and extra-classical receptive field properties and the statistics of natural scenes. Network: Computation in Neural Systems.
[74]
Ziegaus, C., and Lang, E. W. 1997. Statistics of natural and urban images. In Proc. 7th International Conference on Artificial Neural Networks, Springer-Verlag, Berlin, vol. 1327 of Lecture Notes in Computer Science, 219--224.
[75]
Ziegaus, C., and Lang, E. W. 1998. Statistical invariances in artificial, natural and urban images. Z. Naturforsch 53a, 1009--1021.
[76]
J J Atick and N A Redlich. What does the retina know about natural scenes? Neural Computation, 4:196--210, 1992.
[77]
R J Baddeley and P J B Hancock. A statistical analysis of natural images matches psychophysically derived orientation tuning curves. Proceedings of the Royal Society of London B, 246:219--223, 1991.
[78]
Anthony J Bell and Terrence J Sejnowski. Edges are the 'independent components' of natural scenes. Advances in Neural Information Processing Systems, 9, 1996.
[79]
Anthony J Bell and Terrence J Sejnowski. The independent components of natural scenes are edge filters. Vision Research, 37:3327--3338, 1997.
[80]
T Bossomaier and A W Snyder. Why spatial frequency processing in the visual cortex? Vision Research, 26:1307--1309, 1986.
[81]
G J Burton and Ian R Moorhead. Color and spatial structure in natural scenes. Applied Optics, 26(1):157--170, 1987.
[82]
J Cohen. Dependency of the spectral reflectance curves of the munsell color chips. Psychonomic Science, 1:369--370, 1964.
[83]
Mark E Crovella and Murad S Taqqu. Estimating the heavy tail index from scaling properties. Methodology and Computing in Applied Probability, 1(1):55--79, 1999.
[84]
Dawei W Dong and Joseph J Atick. Statistics of natural time-varying images. Network: Computation in Neural Systems, 6(3):345--358, 1995.
[85]
David J Field. Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 4(12):2379--2394, 1987.
[86]
David J Field. Scale-invariance and self-similar 'wavelet' transforms: An analysis of natural scenes and mammalian visual systems. In M Farge, J C R Hunt, and J C Vassilicos, editors, Wavelets, fractals and Fourier transforms, pages 151--193. Clarendon Press, Oxford, 1993.
[87]
David J Field and Nuala Brady. Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes. Vision Research, 37(23):3367--3383, 1997.
[88]
Peter J B Hancock, Roland J Baddeley, and Leslie S Smith. The principle components of natural images. Network, 3:61--70, 1992.
[89]
Frederic J Harris. On the use of windows for harmonic analysis with the discrete fourier transform. Proceedings of the IEEE, 66(1):51--84, 1978.
[90]
H van Hateren and A van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London B, 265:359--366, 1998.
[91]
Bruce M Hill. A simple general approach to inference about the tail of a distribution. The Annals of Statistics, 3(5):1163--1174, 1975.
[92]
Jarmo Hurri, Aapo Hyvärinen, and Erkki Oja. Wavelets and natural image statistics. In Proceedings of the 10 th Scandinavian Conference on Image Analysis, pages 13--18, June 1997.
[93]
Aapo Hyvärinen. Survey on independent components analysis. Neural Computing Surveys, 2:94--128, 1999.
[94]
Deane B Judd, David L MacAdam, and Günther Wyszecki. Spectral distribution of typical light as a function of correlated color temperature. Journal of the Optical Society of America, 54(8):1031--1040, 1964.
[95]
Michael S Langer. Large-scale failures of f −α scaling in natural image spectra. Journal of the Optical Society of America A, 17(1):28--33, 2000.
[96]
Hsien-Che Lee. Internet color imaging. In Proceedings of the SPIE, volume 3080, pages 122--135, 2000.
[97]
Hsien-Che Lee. Introduction to color imaging science. Cambridge University Press, Cambridge, 2005.
[98]
Laurence T Maloney. Evaluation of linear models of surface spectral reflectance with small number of parameters. Journal of the Optical Society of America A, 3:1673--1683, 1986.
[99]
Laurence T Maloney and Brian A Wandell. Color constancy: A method for recovering surface spectral reflectance. Journal of the Optical Society of America A, 3(1):29--33, 1986.
[100]
Chrysostomos L Nikias and Athina P Petropulu. Higher-order spectra analysis. Signal Processing Series. Prentice Hall, 1993.
[101]
B A Olshausen and D J Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607--609, 1996.
[102]
C A Párraga, G Brelstaff, T Troscianko, and I R Moorhead. Color and luminance information in natural scenes. Journal of the Optical Society of America A, 15(3):563--569, 1998.
[103]
C A Párraga, T Troscianko, and D J Tolhurst. The human visual system is optimised for processing the spatial information in natural visual images. Current Biology, 10(1):35--38, 2000.
[104]
F H G Pitt and E W H Selwyn. Colour of outdoor photographic objects. The Photographic Journal, 78:115--121, 1938.
[105]
Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision, 40(1):49--71, 2000.
[106]
Stéphane J M Rainville and Frederick A A Kingdom. Spatial-scale contribution to the detection of mirror symmetry in fractal noise. Journal of the Optical Society of America A, 16(9):2112--2123, 1999.
[107]
D L Ruderman and W Bialek. Statistics of natural images: Scaling in the woods. Physical Review Letters, 73(6):814--817, 1994.
[108]
Daniel L Ruderman. The statistics of natural images. Network: Computation in Neural Systems, 5(4):517--548, 1997.
[109]
A van der Schaaf. Natural image statistics and visual processing. PhD thesis, Rijksuniversiteit Groningen, The Netherlands, March 1998.
[110]
Eero P Simoncelli. Bayesian denoising of visual images in the wavelet domain. In P Müller and B Vidakovic, editors, Bayesian Inference in Wavelet Based Models, volume 141 of Lecture Notes in Statistics, pages 291--308, New York, 1999. Springer-Verlag.
[111]
Eero P Simoncelli. Modelling the joint statistics of images in the wavelet domain. In Proceedings of the 44th SPIE Anual Meeting, volume 3813, pages 188--195, July 1999.
[112]
Eero P Simoncelli and Javier Portilla. Texture characterization via joint statistics of wavelet coefficient magnitudes. In Proceedings of the 5th International Conference on Image Processing, October 1998.
[113]
Mitchell G A Thomson. Higher-order structure in natural scenes. Journal of the Optical Society of America A, 16(7):1549--1553, 1999.
[114]
Mitchell G A Thomson. Visual coding and the phase structure of natural scenes. Network: Computation in Neural Systems, 10(2):123--132, 1999.
[115]
D J Tolhurst, Y Tadmor, and T Chiao. Amplitude spectra of natural images. Ophthalmic and Physiological Optics, 12:229--232, 1992.
[116]
Christian Ziegaus and Elmar W Lang. Statistics of natural and urban images. In Proceedings of the 7th International Conference on Artificial Neural Networks, volume 1327, pages 219--224, Berlin, 1997. Springer-Verlag.
[117]
Christian Ziegaus and Elmar W Lang. Statistical invariances in artificial, natural and urban images. Zeitschrift für Naturforschung A, 53a(12):1009--1021, 1998.

Cited By

View all
  • (2014)Exposure control for HDR videoOptics, Photonics, and Digital Technologies for Multimedia Applications III10.1117/12.2051127(913805)Online publication date: 15-May-2014
  • (2014)Ensemble Nyström method for predicting conflict level from speechSignal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific10.1109/APSIPA.2014.7041794(1-5)Online publication date: Dec-2014
  • (2014)High dynamic range imaging technology for micro camera arraySignal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific10.1109/APSIPA.2014.7041726(1-4)Online publication date: Dec-2014
  • Show More Cited By

Index Terms

  1. Image statistics: from data collection to applications in graphics

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SIGGRAPH '10: ACM SIGGRAPH 2010 Courses
      July 2010
      1132 pages
      ISBN:9781450303958
      DOI:10.1145/1837101
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 July 2010

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. computational photography
      2. natural image statistics
      3. rendering

      Qualifiers

      • Research-article

      Conference

      SIGGRAPH '10
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)13
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 15 Oct 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2014)Exposure control for HDR videoOptics, Photonics, and Digital Technologies for Multimedia Applications III10.1117/12.2051127(913805)Online publication date: 15-May-2014
      • (2014)Ensemble Nyström method for predicting conflict level from speechSignal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific10.1109/APSIPA.2014.7041794(1-5)Online publication date: Dec-2014
      • (2014)High dynamic range imaging technology for micro camera arraySignal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific10.1109/APSIPA.2014.7041726(1-4)Online publication date: Dec-2014
      • (2013)Computer generated images vs. digital photographsJournal of Visual Communication and Image Representation10.1016/j.jvcir.2013.08.00924:8(1276-1292)Online publication date: 1-Nov-2013

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media