Image Stitching Implementation Code

This text discusses personally modified code for image stitching implementation that has been refined through iterative improvements. We can further explore the specific modifications made during this process and their impact on the stitching results. The modification approaches may include but are not limited to: algorithm changes (such as switching from SIFT to SURF feature detection), adding or removing feature points through threshold adjustments, parameter tuning (like homography matrix calculation parameters), and blending technique improvements. The implementation typically involves key functions such as feature detection using detectors like ORB or AKAZE, feature matching with RANSAC for outlier removal, perspective transformation using OpenCV's warpPerspective, and multi-band blending for seamless transitions. This image stitching code can be applied to various scenarios including panoramic image generation, medical image processing, satellite imagery analysis, and surveillance system applications. Therefore, we can continue discussing image stitching applications across different domains and investigate methods for further algorithm optimization, such as incorporating deep learning-based feature matching or implementing real-time stitching capabilities through GPU acceleration, to achieve better stitching quality and performance.