The set of images will have consistent exposure between frames to minimize the probability of seams occurring.įeature detection is necessary to automatically find correspondences between images. Additionally, the aspect ratio of a panorama image needs to be taken into account to create a visually pleasing composite.įor panoramic stitching, the ideal set of images will have a reasonable amount of overlap (at least 15–30%) to overcome lens distortion and have enough detectable features. In a non-ideal real-life case, the intensity varies across the whole scene, and so does the contrast and intensity across frames. Other major issues to deal with are the presence of parallax, lens distortion, scene motion, and exposure differences. Other reasons for seams could be the background changing between two images for the same continuous foreground. Since the illumination in two views cannot be guaranteed to be identical, stitching two images could create a visible seam. When multiple images exist in a panorama, techniques have been developed to compute a globally consistent set of alignments and to efficiently discover which images overlap one another.Ī final compositing surface onto which to warp or projectively transform and place all of the aligned images is needed, as are algorithms to seamlessly blend the overlapping images, even in the presence of parallax, lens distortion, scene motion, and exposure differences. Algorithms that combine direct pixel-to-pixel comparisons with gradient descent (and other optimization techniques) can be used to estimate these parameters.ĭistinctive features can be found in each image and then efficiently matched to rapidly establish correspondences between pairs of images. In order to estimate image alignment, algorithms are needed to determine the appropriate mathematical model relating pixel coordinates in one image to pixel coordinates in another. This sample image shows geometrical registration and stitching lines in panorama creation. Multiple-image super-resolution imaging.High-resolution photomosaics in digital maps and satellite imagery.Image stabilization feature in camcorders that use frame-rate image alignment.Image stitching is widely used in modern applications, such as the following: Some digital cameras can stitch their photos internally. Commonly performed through the use of computer software, most approaches to image stitching require nearly exact overlaps between images and identical exposures to produce seamless results, although some stitching algorithms actually benefit from differently exposed images by doing high-dynamic-range imaging in regions of overlap. Image stitching or photo stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama or high-resolution image. The photo on the right is distorted slightly so that it matches up with the one on the left.
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