Image Homography Rectification

Homography rectification is a technique for correcting geometric distortions in images, commonly used for perspective transformation or viewpoint correction of planar objects. This method employs a homography matrix to map coordinates from the source image to a new plane, effectively eliminating perspective distortions through a projective transformation.

Implementing homography rectification in MATLAB typically involves three key computational steps: Feature Point Matching: Corresponding feature points must be identified in both source and target images, typically captured from different viewpoints of the same object. Algorithms like SIFT or SURF can automate this process, though manual selection is also viable. Homography Matrix Calculation: Using matched feature points, the homography matrix is solved through least-squares optimization or robust estimators like RANSAC to handle outliers. This 3x3 matrix encapsulates the projective transformation between image planes. Geometric Transformation Application: The derived homography matrix is applied to the source image using spatial transformations with interpolation methods (e.g., bilinear interpolation) for resampling, generating the rectified output.

The `RectifyImg.m` script encapsulates this workflow into a complete processing pipeline. Users simply provide the original image and corresponding target feature point coordinates to automate the rectification. This approach finds applications in document scanning, license plate recognition, and 3D reconstruction, enhancing subsequent processing accuracy.

Implementation Considerations: Rectification quality directly depends on homography matrix accuracy, requiring well-distributed and precise feature points. For large perspective changes or strong distortions, integrating camera calibration or RANSAC improves robustness. Real-time applications may optimize computational speed through GPU acceleration or precomputed transformation parameters.