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Image sensing model and computer simulation for CCD camera systems

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Abstract

A computational model and a computer simulation system are presented for image sensing in a typical CCD camera system. The computational model makes explicit the sequence of transformations that the light incident on the camera system undergoes before being sensed and recorded. The model is based on a precise definition of input to the camera system that decouples the photometric properties of a scene from the geometric properties of the scene. Based on this model, an interactive research software, the Image Defocus Simulator, has been developed. Application of this software in machine vision research and development is described with examples.

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References

  • Aho AV, Sethi R, Ullman JD (1986) Compilers: principles, techniques, and tools. Addison-Wesley, Reading, Mass

    Google Scholar 

  • Born M, Wolf E (1980) Principles of optics. 6th edn, Pergamon Press, Oxford

    Google Scholar 

  • Chen YC (1987) Lens effect on synthetic image generation based on light particle theory. Comput Graph, pp 347–366

  • Foley JD, van Dam A, Feiner SK, Hughes JF (1990) Computer graphics: principle and practice. 2nd edn, Addison-Wesley, Reading, Mass

    Google Scholar 

  • Goodman JW (1968) Introduction to Fourier optics. McGraw-Hill, San Francisco

    Google Scholar 

  • Healey G, Kondepudy R (1992) Modeling and calibrating CCD cameras for illumination insensitive machine vision. Proceedings of Optics, Illumination, and Image Sensing for Machine Vision VI, SPIE, vol 1614, pp 121–132

    Google Scholar 

  • Hecht E (1987) Optics, 2nd edn, Addison-Wesley, Reading, Mass

    Google Scholar 

  • Hopkins HH (1955) The frequency response of a defocused optical system. Proceeding of the Royal Society of London, A 231:91–103

    Google Scholar 

  • Horn BKP (1986) Robot vision. McGraw-Hill, New York

    Google Scholar 

  • Levine MD (1985) Vision in man and machine. McGraw-Hill, New York

    Google Scholar 

  • Lu MC (1993) Computer modeling and simulation techniques for computer vision problems. Ph.D. dissertation. Department of Electrical Engineering, State University of New York at Stony Brook, N.Y

    Google Scholar 

  • Lu MC and Subbarao M (1992) Image defocus simulator: a software tool. Technical Report No. 92.05.27, Computer Vision Laboratory, Department of Electrical Engineering, State University of New York, Stony Brook, N.Y

    Google Scholar 

  • Oppenheim AV, Schafer RW (1989) Discrete-time Signal Processing. Prentice-Hall, Englewood Cliffs, N.J

    Google Scholar 

  • Potmesil M, Chakravarty I (1982) Synthetic image generation with a lens and aperture camera model. ACM Trans Graph 1:85–108

    Google Scholar 

  • Rosenfeld A, Kak AC (1982) Digital picture processing. 2nd edn, Academic Press, New York

    Google Scholar 

  • Shafer SA (1988) Automation and calibration for robot vision systems. Technical Report No. CMU-CS-88-147, Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania

    Google Scholar 

  • Sproson WN (1983) Colour science in television and display systems. Adam Hilger, Bristol

    Google Scholar 

  • Subbarao M (1988) Efficient depth recovery through inverse optics. In: Freeman H (ed) Machine vision for inspection and measurement. Academic Press, Boston, pp 101–126

    Google Scholar 

  • Subbarao M (1990) On the depth information in the point spread function of a defocused optical system. Technical Report No. 90.02.07, Computer Vision Laboratory, Department of Electrical Engineering, State University of New York, Stony Brook, N.Y

    Google Scholar 

  • Subbarao M, Nikzad A (1990) Model for image sensing and digitization in computer vision. Proceedings of SPIE Conference, OE/90, Boston, vol 385, pp 70–84

    Google Scholar 

  • Subbarao M, Surya G (1992) Application of spatial-domain convolution/deconvolution transform for determining distance from image defocus. Technical Report No. 92.01.18, Computer Vision Laboratory, Department of Electrical Engineering, State University of New York, Stony Brook, N.Y

    Google Scholar 

  • Subbarao M, Wei TC (1992) Depth from defocus and rapid autofocusing: a practical approach. Technical Report No. 92.01.17, Computer Vision Laboratory, Department of Electrical Engineering, State University of New York, Stony Brook, N.Y (An abbreviated version appears in Proceedings of the IEEE Computer Vision and Pattern Recognition '92:773-776)

    Google Scholar 

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Subbarao, M., Lu, MC. Image sensing model and computer simulation for CCD camera systems. Machine Vis. Apps. 7, 277–289 (1994). https://doi.org/10.1007/BF01213418

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