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Lecture notes from university.
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Neighborhood processing.html (2108B)


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      3 <html><head><link rel="stylesheet" href="sitewide.css" type="text/css"><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/><meta name="exporter-version" content="Evernote Mac 7.5.2 (457164)"/><meta name="altitude" content="0.3140121698379517"/><meta name="author" content="Alex Balgavy"/><meta name="created" content="2017-12-19 19:57:01 +0000"/><meta name="latitude" content="52.37337526752695"/><meta name="longitude" content="4.836118090339202"/><meta name="source" content="desktop.mac"/><meta name="updated" content="2017-12-19 20:10:29 +0000"/><title>Neighborhood processing</title></head><body><div>Value of pixel in output is determined by value of same pixel in input and the neighbours.</div><div>Use small neighbourhood of pixel in input to get new brightness value in output.</div><div><br/></div><div><img src="Neighborhood%20processing.resources/screenshot.png" height="181" width="396"/><br/></div><div><br/></div><div><br/></div><div><span style="font-weight: bold;">Filtering (applying filter or mask to entire image):</span></div><ul><li>Mean filter — replace noise pixel by mean value of neighbours, including itself</li><li>Median filter — order values in increasing order including itself, find median, set that as new value</li></ul><div><br/></div><div><span style="font-weight: bold;">Correlation/convolution in an image:</span></div><ul><li>works by scanning through image and applying mask (“kernel”) to each pixel</li><li>kernel is filled with numbers, not always equal to one (“kernel coefficients”)</li><li>coefficients weigh pixel value they are covering</li><li>output of correlation is sum of weighted pixel values</li><li>kernel alters center pixel</li><li>decreases size of image!</li></ul><div><br/></div><div>example kernels:</div><div><img src="Neighborhood%20processing.resources/screenshot_1.png" height="210" width="578"/><br/></div><div><br/></div><div><br/></div></body></html>