Corner Detector

This function block specializes in detecting corner patterns within images. Utilizing well-known algorithms, it provides an efficient means to identify significant points of interest that can be useful in various image processing tasks.

📥 Inputs

Image Any This input accepts any image data for corner detection.

📤 Outputs

Detected Corners The output shows the original image with detected corners highlighted.

Number of Corners This output indicates the total number of corners detected in the input image.

Locations This output provides the exact locations of detected corners as coordinate points.

🕹️ Controls

Detector Type A dropdown menu that allows you to select the corner detection algorithm to be used, either "HARRIS CORNER" or "SHI-THOMAS CORNER".

Threshold A slider to adjust the sensitivity of corner detection. Higher values may yield fewer corners, while lower values may return more corners.

🎨 Features

Multiple Detection Methods Users can choose between different corner detection algorithms based on their specific needs.

Visual Output Corners are displayed on the image using colored circles, providing clear visual feedback on the detected features.

📝 Usage Instructions

1. Connect Input Image: Link an image to the Image Any input for analysis.

2. Select Detector Type: Choose the desired corner detection algorithm from the Detector Type dropdown.

3. Adjust Threshold: Use the Threshold slider to set the sensitivity of the corner detection process.

4. Run the Block: Evaluate the block to perform corner detection. The output will include the image with detected corners, the count of corners, and their respective locations.

📊 Evaluation

When executed, this function block will process the input image, highlight detected corners, and provide relevant numerical and coordinate data on the output.

💡 Tips and Tricks

Tuning the Detection

To enhance corner detection, experiment with different threshold values to find the most effective sensitivity for your specific image.

Pre-processing the Input Image

Consider using a Blur or Image Threshold block beforehand to reduce noise in the image, which can improve detection accuracy.

Visualize Results with Different Data Types

You can connect this block's Locations output to other blocks that perform further processing based on corner locations, such as Draw Point or Measure Position Distance.

🛠️ Troubleshooting

No Corners Detected

If no corners are detected, ensure that the input image is clear and contains distinct features. Adjust the threshold for detection sensitivity.

Threshold Issues

Ensure the threshold value follows the acceptable range. If there is an error regarding the threshold, adjust it to a more suitable number.