Edge detection: Difference between revisions

Revision as of 05:18, 8 April 2005

Revision as of 21:52, 11 May 2005

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The goal of '''edge detection''' is to mark the points in an image at which the intensity changes sharply. Sharp changes in image properties usually reflect important events and changes in world properties. Edge detection is a research field within [[Computercomputer vision]] and [[Featurefeature extraction]].

== Edge properties ==

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Many edge-detection operators are based upon the 1st derivative of the intensity - this gives us the intensity gradient of the original data. Using this information we can search an image for peaks in the intensity gradient.

If ''I''(''x'') represents the intensity of pixel ''x'', and ''I''′(''x'') represents the first derivative (intensity gradient) at pixel ''x'', we therefore find that:

''I''′(''x'') = −1·''I''(''x'' − 1) + 0·''I''(''x'') + 1·''I''(''x'' + 1)

I'(x) = -1.I(x-1) + 0.I(x) + 1.I(x+1)

For higher performance image processing, the 1st derivative can therefore be calculated (in 1D) by [[convolution|convolving]] the original data with a mask:

<center><table border="1" cellpadding=3>

<tr><td>-&minus;1</td><td>0</td><td>1</td></tr>

</table></center>

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If I(x) represents the intensity at point x, and I"(x) is the second derivative at point x:

:I"(x) = 1.I(x-1) - 2.I(x) + 1.I(x+1)

Again most algorithms use a convolution mask to quickly process the image data: