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Harris Corner Filter
The
Harris Corner Filter node in AugeLab Studio applies the Harris corner filtering algorithm to detect corners in an input image. It highlights the detected corners and provides the corner size (number of corners) and their positions as output.The
Harris Corner Filter node applies the Harris corner filtering algorithm to an input image. It identifies corners in the image and provides the following outputs:- The image with highlighted corners.
- The number of corners detected (corner size).
- The positions of the detected corners.
- Node Title: Harris Corner Filter
- Node ID: OP_NODE_HARRIS_CORNER
The
Harris Corner Filter node has the following input socket:- 1.Input Image: The input image on which the Harris corner filtering algorithm will be applied.
The
Harris Corner Filter node has the following output sockets:- 1.Filtered Image: The input image with highlighted corners.
- 2.Corner Size: The number of corners detected in the image.
- 3.Corner Positions: The positions of the detected corners.
The
Harris Corner Filter node provides the following parameters for configuration:- Pixel Threshold: The threshold value for classifying pixels as corners. Increase this value to detect stronger corners and decrease it to detect weaker corners.
- Block Size: The size of the neighborhood considered for corner detection. It affects the level of noise suppression. Larger values result in smoother corner regions.
- Aperture Size: The size of the Sobel operator aperture used to compute image gradients. It determines the level of details captured in the corner detection process.
- Harris Free Parameter: The free parameter (k) in the Harris corner response equation. Adjusting this parameter influences the sensitivity of the corner detector. Higher values result in fewer corners being detected.
- 1.Drag and drop the
Harris Corner Filternode from the node library onto the canvas in AugeLab Studio. - 2.Connect the input image to the Input Image input socket of the
Harris Corner Filternode. - 3.Configure the desired parameters of the
Harris Corner Filternode, such as the pixel threshold, block size, aperture size, and Harris free parameter. - 4.Run the pipeline.
- 5.The
Harris Corner Filternode will apply the Harris corner filtering algorithm to the input image and highlight the detected corners in the output image. - 6.The Filtered Image output will contain the input image with the highlighted corners.
- 7.The Corner Size output will provide the number of corners detected in the image.
- 8.The Corner Positions output will provide the positions (x, y coordinates) of the detected corners.
- 9.Retrieve the outputs for further analysis, processing, or visualization.
- The
Harris Corner Filternode applies the Harris corner filtering algorithm to an input image to detect corners. - Adjust the parameters of the node to control the corner detection process.
- Higher pixel threshold values detect stronger corners, while lower values detect weaker corners.
- The block size parameter affects the level of noise suppression. Larger values result in smoother corner regions.
- The aperture size parameter determines the level of details captured in the corner detection process.
- The Harris free parameter influences the sensitivity of the corner detector. Higher values result in fewer corners being detected.
- The outputs include the filtered image with highlighted corners, the number of detected corners, and their positions.
- Use the outputs for further analysis, processing, or visualization.
- Experiment with different parameter values to achieve the desired corner detection results.
- The node supports grayscale and color input images.
- The Harris corner filtering algorithm is widely used for corner detection and feature extraction in computer vision applications.
That concludes the documentation for the
Harris Corner Filter node in AugeLab Studio. Use this node to apply the Harris corner filtering algorithm and detect corners in an input image.