Image Contour Extraction Using Wavelet Transform for Registration

This method employs wavelet transform to extract image contours and perform image registration. Initially, we implement wavelet-based edge detection using multi-resolution analysis (typically with Haar or Daubechies wavelets) to identify significant edges in the image. The algorithm then processes the detected edges to locate the longest continuous contour through connected component analysis or chain coding methods. For each point along the identified contour, we calculate curvature values using differential geometry approaches, such as computing the derivative of tangent angles or employing local curve fitting techniques. These curvature measurements serve as distinctive features for subsequent matching operations. The registration phase involves comparing curvature datasets between reference and target images using correlation methods or optimization algorithms (like RANSAC or least-squares fitting) to determine optimal transformation parameters (including rotation, translation, and scaling factors). This wavelet-based contour registration approach enhances alignment accuracy by leveraging multi-scale edge information and curvature invariants, resulting in superior image matching performance compared to conventional intensity-based methods. The implementation typically involves wavelet decomposition using functions like wavedec2 in MATLAB, edge detection with Canny-like wavelet thresholds, contour processing using bwboundaries or similar functions, and curvature calculation through parametric derivative estimation.