How to Perform Image Stitching
Image stitching is a process used in computer vision to combine multiple images into a single panoramic image. The following steps outline the general approach:
1. Capture Overlapping Images
Begin by capturing a series of images of the scene. Ensure that there is a significant overlap (20%-30%) between consecutive images to facilitate reliable stitching.
2. Feature Detection
Use feature detection algorithms (like SIFT, SURF, or ORB) to identify keypoints and descriptors in each image. These features will help in matching corresponding points across images.
3. Feature Matching
Match the features between adjacent images using techniques such as FLANN or KNN. This step determines how the images relate to each other based on the matched keypoints.
4. Homography Estimation
Calculate a homography matrix using RANSAC or similar methods to estimate the transformation required to align the images accurately.
5. Image Warping
Warp the images based on the estimated homography, aligning them into a common coordinate system. This may involve resizing and rotating images as needed.
6. Blending
Blend the images seamlessly to eliminate visible seams. Techniques like feathering or multi-band blending can be employed to enhance the transition between images.
7. Final Touches
Crop the final image to remove any black areas resulting from the warping process and improve overall aesthetics.
Using libraries like OpenCV in Python simplifies the implementation of these steps, allowing for rapid development and experimentation.