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    Effect of point-spread functions on geometric measurements in computer vision.

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    Author
    White, Raymond Gordon.
    Issue Date
    1991
    Keywords
    Dissertations, Academic
    Imaging systems
    Computer science.
    Advisor
    Schowengerdt, Robert A.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    The point-spread function (PSF) of an imaging system can affect the precision to which geometric features can be measured in digital images. It is shown that the PSF alone, when larger than a certain size relative to the sampling rate, does not cause a loss of precision, but actually improves the precision to which features can be measured. Quantization of the samples in an image is the limiting factor on precision. How much the precision is limited in the face of quantization depends on the size and shape of the PSF. The theory of locales is used extensively to compute bounds on precision as a function of PSF size and shape. Many geometric feature models are considered although almost all of the emphasis is on analysis of the unit step edge. It is shown for the step edge that the size of a PSF has more influence on precision than its shape. The size for which optimal precision occurs is ∼0.7 pixels in "radius". Bounds on precision are also shown for the case of a fixed rectangular sampling PSF with a variable-sized optical PSF and for the case where electronic components introduce asymmetry into the PSF. A method for objective comparison of imaging system PSFs is demonstrated. Other factors influencing precision, including unknown scene contrast and noise, are briefly examined. To illustrate the utility of the results, an experiment is presented showing that approximation of a PSF by a Gaussian PSF results in little loss of precision. Another illustrative experiment shows how the orientation precision of several widely-used first derivative operators are affected by the PSF and shows how the template coefficients can be found to optimize orientation precision for a given PSF.
    Type
    text
    Dissertation-Reproduction (electronic)
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Electrical and Computer Engineering
    Graduate College
    Degree Grantor
    University of Arizona
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    Dissertations

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