A single-clicked image has a limited field of view (FOV), we need to stitch together several image stills to form a mosaic to increase the FOV. Image mosaicing is a very popular way to obtain a wide FOV image of a scene. The basic idea is to capture images as a camera moves and stitch these images together to obtain a single larger image.These multiple image slices can be mosaiced together to give an entire view of a scene.
Below is a higher-level overview of the mosaicing technique implemented:
- Choose first two images in the dataset
- Find matching keypoints(features) between the two images
- Calculate the homography using the matched key- points
- Using this homography, one image is warped to be in the same frame as the other and a new image of all black pixels is created which can fit both images in the new frame
- Repeat step 2 with the current mosaic and the next image, until all the images in the dataset are covered
Panorama 1
Panorama 2
Panorama 3
- The quality of mosaic is deteriorating with increase in the number of stitched images(>15) and which is probably because of the homography error accumulates with each stitch.
- Code giving best result when there is more than 75% of overlap and proper alignment between the consecutive images.
- Need to implement Sequential Bundle Adjustment to reduce the propagating error for better results.