Changes between Version 3 and Version 4 of GSoC/2016/Additional_segmentation_algorithms/mean_shift

Timestamp:

Sep 6, 2016, 2:28:20 PM (8 years ago)

Author:

hao2309

Comment:

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GSoC/2016/Additional_segmentation_algorithms/mean_shift

@@ -80 +80 @@
 = DESCRIPTION =
 
-GRASS GIS has the i.segment which provides the possibility to segment an image into objects. This is a basic step in object-based image analysis (OBIA). Currently, the module only provides one segmentation algorithm: region-growing. The code of i.segment was structured in a way that allows addition of other algorithms. The core of proposed GSoC project would thus be to add a series of these algorithms. It would be more useful and comprehensive to add more segment methods to the i.segment module, such as mean-shift and watershed, which could be used in more types of satellite image processing. Special care should be taken for the whole project to code as efficiently as possible, i.e. to make the code run in reasonable time, even for very large images.
+GRASS GIS has the i.segment which provides the possibility to segment an image into objects. This is a basic step in object-based image analysis (OBIA). Currently, the module only provides one segmentation algorithm: region-growing. The code of i.segment was structured in a way that allows addition of other algorithms. It would be more useful and comprehensive to add mean-shift to the i.segment module.
 
-Mean shift segmentation is a local homogenization technique that is very useful for damping shading or tonality differences in localized objects. For the algorithm implementation of this case, basically the algorithm replaces each pixel with the mean of the pixels in a range-r neighborhood and whose value is within a distance d. The Mean Shift takes usually 3 inputs: 1) A distance function for measuring distances between pixels. Usually the Euclidean distance, but any other well defined distance function could be used. The Manhattan Distance is another useful choice sometimes. 2) A radius. All pixels within this radius (measured according the above distance) will be accounted for the calculation. 3) A value difference. From all pixels inside radius r, we will take only those whose values are within this difference for calculating the mean [4].
+Mean shift segmentation is a local homogenization technique that is very useful for damping shading or tonality differences in localized objects. For the algorithm implementation of this case, basically the algorithm replaces each pixel with the mean of the pixels in a range-r neighborhood and whose value is within a distance d. The Mean Shift usually has 3 important parameters: 1) A distance function for measuring distances between pixels. Usually the Euclidean distance, but any other well-defined distance function could be used. The Manhattan Distance is another useful choice sometimes. 2) A radius (spatial bandwidth). All pixels within this radius (measured according the above distance) will be accounted for the calculation. 3) A value difference (range bandwidth). From all pixels inside radius r, we will take only those whose values are within this difference for calculating the mean.
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