Sheet7ip - Tanta university Faculty of engineering Computer and automatic control department Image processing course Fourth year students Sheet 7 Date

Sheet7ip - Tanta university Faculty of engineering Computer...

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Tanta university Faculty of engineering Computer and automatic control department Image processing course Fourth year students Sheet 7, Date : 23/11/2011 ------------------------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------------------------- Instructor: Dr. Hamed Hemeda Sheet 7 (histogram equalization) 1. Consider a continuous image with intensity values in the range [0, L-1]. If we model the intensity level a pixel can take on as a random variable R and the value that variable takes at any pixel as r, we have the following probability density function (PDF) for the variable: ( ) . By definition, the PDF is the same as the normalized histogram for the image. It is well known in probability theorem that a continuous in r and differentiable mapping (transformation function) ( ) that maps ( ) to ( ) satisfies the equation: ( ) ( ) | | . A mapping of particular importance in image processing has the form: ( ) ( ) ∫ ( ) , where w is a dumpy integration variable. a) Explain the importance of the mapping in image processing b) In which application this mapping is used in image processing, explain.
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  • Fall '15
  • Hamed Hemeda
  • Image processing, Probability distribution, Probability theory, SEPTA Regional Rail, probability density function

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