Skip to main content
SpringerLink
Log in

Photometry

  • Chapter
  • First Online:
Active Lighting and Its Application for Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

  • 795 Accesses

Abstract

Active-lighting sensors project light onto target objects or the scenes to obtain photometric measurements of the reflected light, including travel time and return angle. Therefore, understanding these sensor principles requires knowledge of the photometric characteristics of light and the mechanisms of light reflection. This chapter prepares the reader for detailed principle descriptions in later chapters by explaining various photometric characteristics of light and the definitions of brightness. Furthermore, a method for determining the relationship between the original brightness of light and the measured brightness is described.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lambert JH (18922) Lamberts Photometrie: Photometria, sive De mensura et gradibus luminus, colorum et umbrae (1760), vol 6. W. Engelmann

    Google Scholar 

  2. Strutt JW (1871) Xv. on the light from the sky, its polarization and colour. Lond Edinb. Dublin Philos Mag J Sci 41(271):107–120

    Google Scholar 

  3. Horvath H (2009) Gustav mie and the scattering and absorption of light by particles: historic developments and basics. J Quant Spectrosc Radiat Trans 110(11):787–799

    Article  Google Scholar 

  4. Nicodemus FE, Richmond JC (1977) Geometrical considerations and nomenclature for reflectance

    Google Scholar 

  5. Horn BK, Sjoberg RW (1979) Calculating the reflectance map. Appl Opt 18(11):1770–1779

    Article  Google Scholar 

  6. Horn B, Klaus B, Horn P (1986) Robot vision. MIT Press

    Google Scholar 

  7. Kajiya JT (1986) The rendering equation. In: Proceedings of the 13th annual conference on computer graphics and interactive techniques, pp 143–150

    Google Scholar 

  8. Nayar SK, Ikeuchi K, Kanade T (1991) Shape from interreflections. Int J Comput Vis (IJCV) 6(3):173–195

    Article  Google Scholar 

  9. Seitz SM, Matsushita Y, Kutulakos KN (2005) A theory of inverse light transport. In: Proceedings of the international conference on computer vision (ICCV), vol 2. IEEE, pp 1440–1447

    Google Scholar 

  10. Nayar SK, Krishnan G, Grossberg MD, Raskar R (2006) Fast separation of direct and global components of a scene using high frequency illumination. In: ACM transactions on graphics (TOG), vol 25. ACM, pp 935–944

    Google Scholar 

  11. Finlayson GD, Hordley S, Hubel PM (1998) Recovering device sensitivities with quadratic programming. In: Color and imaging conference, vol 1998. Society for imaging science and technology, pp 90–95

    Google Scholar 

  12. Urban P, Desch M, Happel K, Spiehl D (2010) Recovering camera sensitivities using target-based reflectances captured under multiple led-illuminations. In: Proceedings of workshop on color image processing, pp 9–16

    Google Scholar 

  13. Rump M, Zinke A, Klein R (2011) Practical spectral characterization of trichromatic cameras. In: ACM transactions on graphics (TOG), vol 30. ACM, p 170

    Google Scholar 

  14. Han S, Matsushita Y, Sato I, Okabe T, Sato Y (2012) Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 805–812

    Google Scholar 

  15. DiCarlo JM, Montgomery GE, Trovinger SW (2004) Emissive chart for imager calibration. In: Color and imaging conference, vol 2004. Society for Imaging Science and Technology, pp 295–301

    Google Scholar 

  16. Jiang J, Liu D, Gu J, Süsstrunk S (2013) What is the space of spectral sensitivity functions for digital color cameras? In: 2013 IEEE workshop on applications of computer vision (WACV). IEEE, pp 168–179

    Google Scholar 

  17. Mann S, Picard RW (1995) On being ‘undigital’ with digital cameras: extending dynamic range by combining differently exposed pictures

    Google Scholar 

  18. Mitsunaga T, Nayar SK (1999) Radiometric self calibration. In: Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149), vol 1. IEEE, pp 374–380

    Google Scholar 

  19. Grossberg MD, Nayar SK (2004) Modeling the space of camera response functions. IEEE Trans Pattern Anal Mach Intell (PAMI) 26(10):1272–1282

    Article  Google Scholar 

  20. Debevec PE, Malik J (1997) Recovering high dynamic range radiance maps from photographs. In: Proceedings of the 24th annual conference on computer graphics and interactive techniques, pp 369–378

    Google Scholar 

  21. Tsin Y, Ramesh V, Kanade T (2001) Statistical calibration of CCD imaging process. In: Proceedings of the international conference on computer vision (ICCV), pp 480–487

    Google Scholar 

  22. Chang YC, Reid JF (1996) RGB calibration for color image analysis in machine vision. IEEE Trans Image Process (TIP) 5(10):1414–1422

    Article  Google Scholar 

  23. Nayar SK, Mitsunaga T (2000) High dynamic range imaging: spatially varying pixel exposures. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), vol 1. IEEE, pp 472–479

    Google Scholar 

  24. Pal C, Szeliski R, Uyttendale M, Jojic N (2004) Probability models for high dynamic range imaging. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 173–180

    Google Scholar 

  25. Grossberg M, Nayar S (2003) Determining the camera response from images: What is knowable? IEEE Trans Pattern Anal Mach Intell (PAMI) 25(11):1455–1467

    Article  Google Scholar 

  26. Kim SJ, Pollefeys M (2004) Radiometric alignment of image sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 645–651

    Google Scholar 

  27. Litvinov A, Schechner YY (2005) Addressing radiometric nonidealities: a unified framework. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 52–59

    Google Scholar 

  28. Mann S (2001) Comparametric imaging: estimating both the unknown response and the unknown set of exposures in a plurality of differently exposed images. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 842–849

    Google Scholar 

  29. Lin S, Gu J, Yamazaki S, Shum HY (2004) Radiometric calibration from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 938–945

    Google Scholar 

  30. Lin S, Zhang L (2005) Determining the radiometric response function from a single grayscale image. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 66–73

    Google Scholar 

  31. Grossberg M, Nayar S (2003) What is the space of camera response functions? In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 602–609

    Google Scholar 

  32. Wilburn B, Xu H, Matsushita Y (2008) Radiometric calibration using temporal irradiance mixtures. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  33. Shi B, Matsushita Y, Wei Y, Xu C, Tan P (2010) Self-calibrating photometric stereo. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1118–1125

    Google Scholar 

  34. Horn BK (1975) Obtaining shape from shading information. The psychology of computer vision, pp 115–155

    Google Scholar 

  35. Woodham RJ (1980) Photometric method for determining surface orientation from multiple images. Opti Eng 19(1):191139

    Google Scholar 

  36. Romeiro F, Vasilyev Y, Zickler T (2008) Passive reflectometry. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp 859–872

    Google Scholar 

  37. Wong KYK, Schnieders D, Li S (2008) Recovering light directions and camera poses from a single sphere. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp 631–642

    Google Scholar 

  38. Ackermann J, Fuhrmann S, Goesele M (2013) Geometric point light source calibration. In: Vision, modeling and visualization, pp 161–168

    Google Scholar 

  39. Kato K, Sakaue F, Sato J (2010) Extended multiple view geometry for lights and cameras from photometric and geometric constraints. In: Proceedings of the international conference on pattern recognition (ICPR). IEEE, pp 2110–2113

    Google Scholar 

  40. Santo H, Waechter M, Samejima M, Sugano Y, Matsushita Y (2018) Light structure from pin motion: simple and accurate point light calibration for physics-based modeling. In: Proceedings of the European conference on computer vision (ECCV), pp 3–18

    Google Scholar 

  41. Rykowski R, Kostal H (2008) Novel approach for led luminous intensity measurement. In: Light-emitting diodes: research, manufacturing, and applications XII, vol 6910. International Society for Optics and Photonics, p 69100C

    Google Scholar 

  42. Simons RH, Bean AR (2008) Lighting engineering: applied calculations. Routledge

    Google Scholar 

  43. Park J, Sinha SN, Matsushita Y, Tai Y, Kweon I (2014) Calibrating a non-isotropic near point light source using a plane. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2267–2274. https://doi.org/10.1109/CVPR.2014.290

  44. Scarzanella MV, Kawasaki H (2015) Simultaneous camera, light position and radiant intensity distribution calibration. In: Pacific-rim symposium on image and video technology (PSVIT), pp 557–571. https://doi.org/10.1007/978-3-319-29451-3_44

  45. Gardner IC (1947) Validity of the cosine-fourth-power law of illumination. J Res Natl Bur Stand 39(3):213–219

    Article  Google Scholar 

  46. Moreno I, Sun CC (2008) Modeling the radiation pattern of leds. Opt Express 16(3):1808–1819

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Applied Robotics Research, Microsoft Corporation, Redmond, WA, USA

    Katsushi Ikeuchi

  2. Graduate School of Information Science and Technology, Osaka University, Osaka, Japan

    Yasuyuki Matsushita

  3. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan

    Ryusuke Sagawa

  4. Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan

    Hiroshi Kawasaki

  5. Division of Information Science, Nara Institute of Science and Technology, Nara, Japan

    Yasuhiro Mukaigawa

  6. Faculty of Information Sciences, Hiroshima City University, Hiroshima, Japan

    Ryo Furukawa

  7. Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan

    Daisuke Miyazaki

Authors
  1. Katsushi Ikeuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasuyuki Matsushita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryusuke Sagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroshi Kawasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yasuhiro Mukaigawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ryo Furukawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daisuke Miyazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katsushi Ikeuchi .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ikeuchi, K. et al. (2020). Photometry. In: Active Lighting and Its Application for Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-56577-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-56577-0_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-56576-3

  • Online ISBN: 978-3-030-56577-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation