Harris Corner Filter

This function block implements the Harris Corner Detection method, which is used to find corners in images. It allows users to apply corner detection and adjust various parameters to customize the detection process.

📥 Inputs

Image Any The input image where corners are to be detected.

📤 Outputs

Image Any The output image with detected corners highlighted.

Corner Size A number representing the total count of corners detected in the input image.

Corner Positions The specific coordinates of detected corners, allowing for multiple outputs.

🕹️ Controls

Pixel Threshold A slider to set the sensitivity for corner detection. Adjusting this threshold affects how many corners are identified.

Block Size This slider controls the size of the neighborhood used for corner detection. A larger block size can help detect more prominent corners but may miss smaller ones.

Aperture Size This slider sets the aperture size for the Sobel operator used in corner detection. Increasing it can enhance the detection of features at the cost of detail.

Harris Free Parameter This slider controls the sensitivity of the Harris detector. Higher values may increase the chance of detecting corners in images by making it less sensitive to variations.

🎨 Features

Robust Corner Detection Uses the Harris method, which is effective for identifying corners in various conditions.

Real-Time Adjustability The sliders allow for dynamic adjustments to corner detection parameters, providing immediate feedback on their effect.

Visual Feedback Detected corners are marked on the output image for easy visualization.

📝 Usage Instructions

1. Input Image: Connect a suitable image to the Image Any input.

2. Adjust Parameters: Use the sliders to set the Pixel Threshold, Block Size, Aperture Size, and Harris Free Parameter to your preference.

3. Run the Block: Execute the block to perform corner detection and retrieve the output.

📊 Evaluation

Upon execution, this function block will analyze the input image, identify corners based on the specified parameters, and output the processed image with highlighted corners.

💡 Tips and Tricks

Improving Corner Detection

To improve detection, use a higher pixel threshold if too many false corners are being detected. Conversely, lower it if you want to capture more corners.

Preprocessing the Input Image

For better results, consider applying a Blur filter to the input image before running this block to reduce noise that may affect corner detection.

Experiment with Aperture Size

Adjust the Aperture Size as it has a significant impact on edge detection quality. It may take trial and error to find optimal settings for different images.

Use in Conjunction with Other Blocks

This block can be combined with Draw Points after to visualize detected corners directly on your images.

🛠️ Troubleshooting

Insufficient Corners Detected

If corners are not detected, consider reducing the pixel threshold or adjusting the block size parameters. Higher values in these settings can sometimes overlook subtle details.

Too Many False Positives

If too many corners are detected that are not actually corners, increase the pixel threshold to filter out these less confident detections.