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Mean Shift Filtering
The
Mean Shift Filtering node in AugeLab Studio applies the mean shift filtering algorithm to an RGB image.The
Mean Shift Filtering node performs a non-linear spatial filtering operation on an RGB image. It segments the image based on color similarity and spatial proximity. This filtering algorithm is effective for image denoising, segmentation, and texture analysis.- Node Title: Mean Shift Filtering
- Node ID: OP_NODE_MSF
The
Mean Shift Filtering node has the following input socket:- ImageRGB: The input RGB image to be filtered. Connect an RGB image to this socket.
The
Mean Shift Filtering node has the following output socket:- ImageRGB: The filtered RGB image.
The
Mean Shift Filtering node has the following adjustable parameters:- Spatial Radius: Controls the spatial window size for color clustering. Larger values include pixels farther from the current pixel in the spatial domain.
- Color Window Radius: Controls the color window size for color clustering. Larger values include pixels with more different colors in the color domain.
- Maximum Level Radius: Controls the number of downsampling levels in the Gaussian pyramid for the mean shift operation. Larger values result in more downsampling levels and can help capture larger color regions.
- 1.Drag and drop the
Mean Shift Filteringnode from the node library onto the canvas in AugeLab Studio. - 2.Connect the input RGB image to the ImageRGB input socket of the
Mean Shift Filteringnode. - 3.Adjust the parameters Spatial Radius, Color Window Radius, and Maximum Level Radius as desired.
- 4.Run the pipeline.
- 5.The
Mean Shift Filteringnode will apply the mean shift filtering algorithm to the input RGB image using the specified parameters and provide the filtered RGB image at the ImageRGB output socket. - 6.Retrieve the filtered RGB image from the output socket for further processing or visualization.
- The
Mean Shift Filteringnode works specifically with RGB images. - The mean shift filtering algorithm segments the image based on color similarity and spatial proximity. It effectively groups pixels with similar colors and suppresses noise in the image.
- Adjusting the parameters Spatial Radius, Color Window Radius, and Maximum Level Radius can impact the filtering result. Experiment with different parameter values to achieve the desired filtering effect.
- The
Mean Shift Filteringnode is useful for tasks such as image denoising, segmentation, and texture analysis.
That concludes the documentation for the
Mean Shift Filtering node in AugeLab Studio. This node allows you to apply the mean shift filtering algorithm to an RGB image, resulting in a filtered RGB image as output.