Image Mosaicking Using Corner Point Information and Perspective Transformation in MATLAB

Image mosaicking based on corner point information and perspective transformation in MATLAB involves exploring its implementation principles and application scenarios. The implementation approach typically includes detecting distinctive corner points using algorithms like Harris corner detector or FAST (Features from Accelerated Segment Test), followed by applying perspective transformation to seamlessly stitch multiple images together. This method finds applications in various domains such as cartography, autonomous vehicles, and medical imaging. In map production, the technique can mosaic multiple satellite images to create comprehensive maps by aligning overlapping regions through feature matching. For autonomous driving systems, it enables the stitching of multiple camera feeds to enhance the vehicle's field of view and safety by implementing homography matrix calculations. In medical imaging, the method assists in combining multiple scan images to help physicians better observe pathological conditions through precise image registration. Key MATLAB functions involved may include corner(), detectSURFFeatures(), estimateGeometricTransform(), and fitgeotrans() for coordinate transformation. The core algorithm typically follows these steps: corner point detection, feature matching using descriptors like SURF or SIFT, homography estimation via RANSAC algorithm, and image warping using projective transformation. Overall, image mosaicking based on corner point information and perspective transformation in MATLAB serves as a powerful tool with extensive applications across multiple technical domains.