MeanShiftSmoothing - Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing)¶
Perform mean shift filtering
Detailed description¶
This application performs mean shift fitlering (multi-threaded).
Parameters¶
This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is MeanShiftSmoothing .
[1] | Table: Parameters table for Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing). |
Parameter Key | Parameter Name | Parameter Type |
---|---|---|
in | Input Image | Input image |
fout | Filtered output | Output image |
foutpos | Spatial image | Output image |
ram | Available RAM (Mb) | Int |
spatialr | Spatial radius | Int |
ranger | Range radius | Float |
thres | Mode convergence threshold | Float |
maxiter | Maximum number of iterations | Int |
rangeramp | Range radius coefficient | Float |
modesearch | Mode search. | Boolean |
inxml | Load otb application from xml file | XML input parameters file |
outxml | Save otb application to xml file | XML output parameters file |
- Input Image: The input image.
- Filtered output: The filtered output image.
- Spatial image: The spatial image output. Spatial image output is a displacement map (pixel position after convergence).
- Available RAM (Mb): Available memory for processing (in MB).
- Spatial radius: Spatial radius of the neighborhood.
- Range radius: Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space.
- Mode convergence threshold: Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations.
- Maximum number of iterations: Algorithm iterative scheme will stop if convergence hasn’t been reached after the maximum number of iterations.
- Range radius coefficient: This coefficient makes dependent the ranger of the colorimetry of the filtered pixel : y = rangeramp*x+ranger.
- Mode search.: If activated pixel iterative convergence is stopped if the path crosses an already converged pixel. Be careful, with this option, the result will slightly depend on thread number.
- Load otb application from xml file: Load otb application from xml file.
- Save otb application to xml file: Save otb application to xml file.
Example¶
To run this example in command-line, use the following:
otbcli_MeanShiftSmoothing -in maur_rgb.png -fout smooth.tif -foutpos position.tif -spatialr 16 -ranger 16 -thres 0.1 -maxiter 100
To run this example from Python, use the following code snippet:
#!/usr/bin/python
# Import the otb applications package
import otbApplication
# The following line creates an instance of the MeanShiftSmoothing application
MeanShiftSmoothing = otbApplication.Registry.CreateApplication("MeanShiftSmoothing")
# The following lines set all the application parameters:
MeanShiftSmoothing.SetParameterString("in", "maur_rgb.png")
MeanShiftSmoothing.SetParameterString("fout", "smooth.tif")
MeanShiftSmoothing.SetParameterString("foutpos", "position.tif")
MeanShiftSmoothing.SetParameterInt("spatialr", 16)
MeanShiftSmoothing.SetParameterFloat("ranger", 16)
MeanShiftSmoothing.SetParameterFloat("thres", 0.1)
MeanShiftSmoothing.SetParameterInt("maxiter", 100)
# The following line execute the application
MeanShiftSmoothing.ExecuteAndWriteOutput()
Limitations¶
With mode search option, the result will slightly depend on thread number.
Authors¶
This application has been written by OTB-Team.