Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration.

Area of Science:

• Computer Vision
• Image Processing
• Metrology

Background:

• Precise camera calibration is crucial for accurate measurements in various applications.
• Manual calibration procedures are time-consuming and prone to errors.
• Automated solutions are needed to streamline the calibration process.

Purpose of the Study:

• To develop an automatic algorithm for camera calibration.
• To reduce the time and effort required for camera calibration procedures.
• To improve the accuracy of measurements performed on objects within images.

Main Methods:

• A novel algorithm automatically detects a chessboard-like pattern in images without prior knowledge of its dimensions.
• The detection process combines analysis of Hough transforms, image corners, and invariant properties of perspective transformation.
• The algorithm is designed for robust performance in diverse imaging conditions.

Main Results:

• The proposed algorithm successfully identifies the calibration pattern automatically.
• Comparative analysis shows the algorithm's viability against commonly used methods.
• The automated approach significantly reduces calibration time.

Conclusions:

• The developed algorithm offers a valuable tool for efficient and accurate camera calibration.
• This method has the potential to enhance applications requiring precise image-based measurements.
• Further research can explore its integration into real-time measurement systems.