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Interactive Watershed

32 bytes removed, 09:34, 6 May 2017
H-Maxima amd H-Watershed: Improve grammar, fix typos
==H-Maxima amd H-Watershed==
An image local maximum (minimum) is a region in the image for which all neighboring pixels have a lower (higher) value. A lot of maxima in an image are simply cause caused by noise and are not meaningful. If one can select more significant maxima corresponding to objects They , they could be used to initiate a watershed segmentation of the these objects.
There exist many different way ways of detecting local maxima. H-maxima focus on maxima robustness. It has one paramterparameter, H, that can be compared to the intensity of the noise in the image. H-Maxima maxima selects all maxima which are still maxima when there their value is decreased by H. When H is increased , less maxima are selected but they end being more robust to noise. Image 3 shows the maxima selected in a sample image for different H values.
We call H-Watershed the segmentation obtained by flooding the H-Maxima maxima of an image. Such segmentation has 2 parameters. The Threshold, T, that stops the flooding of the image and the H-maxima robustness, H, that indicate where the flooding is initiated. File:Figure_HMax_HWatershed_v3.PNG
[[File:Figure_HMax_HWatershed_v3.PNG|center|800px]]
<div align="center">'''Figure 3''' The figure illustrates the detection of H-Maxima for in 2D, 1D and the resulting 2D watershed (repectively row 1, 2 and 3). The 1st column show shows the original data, whereas column 2, 3 and 4 show the maxima and H-Watershed respectively for H value 0, 10 and 40. The threshold value 100 was used for the H-Watershed on row 3. The image used for the illustration is crop of the blobs image available from Fiji ImageJ sample image.</div>
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