Image Coordinate Extraction in Zhang's Camera Calibration Method with Algorithm Enhancements

In Zhang's camera calibration method, image coordinate extraction serves as a critical step that maps physical quantities from images to the camera coordinate system. The traditional implementation typically involves using corner detection algorithms like Harris corner detector or Shi-Tomasi method to identify checkerboard pattern points, followed by sub-pixel refinement techniques to enhance coordinate accuracy. However, this approach has demonstrated certain limitations, particularly in handling image distortion with insufficient accuracy. Recently, a novel image coordinate extraction method has been proposed that integrates deep learning with traditional geometric approaches. This method employs convolutional neural networks (CNN) to predict image coordinates, typically implemented using frameworks like TensorFlow or PyTorch, where the network architecture might include specialized layers for coordinate regression. The CNN predictions are then compared and fused with results from conventional geometric methods through weighted averaging or adaptive fusion algorithms. This hybrid approach significantly reduces error margins while improving precision, particularly through the network's ability to learn distortion patterns from training data. The method has demonstrated exceptional performance in handling image distortion scenarios and is poised to become one of the mainstream approaches in camera calibration systems. Implementation typically involves training the neural network on diverse datasets containing various distortion types, followed by integration with existing calibration pipelines using OpenCV or similar computer vision libraries.