→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 by noise and are not meaningful. If one can select more significant maxima corresponding to objects They could be used to initiate a watershed segmentation of the these objects.
There exist many different
way of detecting local maxima. H-maxima focus on maxima robustness. It has one paramter, H, that can be compared to the intensity of the noise in the image. H- Maxima selects all maxima which are still maxima when there 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 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
<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 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 sample image.</div>