Differences between revisions 3 and 5 (spanning 2 versions)

Image statistics

The image is characterised by a probability density function (PDF). The PDF f describes the probability of the occurrence of a discrete grey level q in the range of grey levels Q.

latex error! exitcode was 2 (signal 0), transscript follows:

Example

The following code calculates the PDF pdf for a byte image img in the intervall [0,255]

   1 h=histogram(img,bins=256,range=[0,255],normed=True)
   2 pdf,x=h[0],h[1]

The expression normed=True is used for the normalization of the PDF.

latex error! exitcode was 2 (signal 0), transscript follows:

The anti-derivative of the PDF is the cumulative density function (CDF). It can be approximated from the cumulative sum

   1 cdf=pdf.cumsum()

-  ⇤ ← Revision 3 as of 2008-12-08 12:29:33 → 
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+   ← Revision 5 as of 2008-12-08 12:38:42 → ⇥
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-Deletions are marked like this.
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-The following code calculates the PDF {{{pdf}}} for a byte image in the intervall {{{[0,255]}}}
{{{#!latex
$\sum_{q=0}^255f_q=1$
}}}
+The following code calculates the PDF {{{pdf}}} for a ''byte'' image {{{img}}} in the intervall {{{[0,255]}}}
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+Line 26:
+The expression {{{normed=True}}} is used for the normalization of the PDF. 
{{{#!latex
$\sum_{q=0}^{255}pdf_q=1$
}}}

The anti-derivative of the PDF is the cumulative density function (CDF). It can be approximated from the cumulative sum
{{{#!python
cdf=pdf.cumsum()
}}}

{{attachment:landsat_b80_pdfcdf.png}}