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Two images stitched together. The photo on the right is distorted slightly so that it matches up with the one on the left. 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.
In photogrammetry and computer stereo vision, bundle adjustment is simultaneous refining of the 3D coordinates describing the scene geometry, the parameters of the relative motion, and the optical characteristics of the camera(s) employed to acquire the images, given a set of images depicting a number of 3D points from different viewpoints.
The problem is made more difficult when the objects in the scene are in motion relative to the camera(s). A typical application of the correspondence problem occurs in panorama creation or image stitching — when two or more images which only have a small overlap are to be stitched into a larger composite image. In this case it is necessary to ...
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.
Image registration or image alignment algorithms can be classified into intensity-based and feature-based. [3] One of the images is referred to as the moving or source and the others are referred to as the target, fixed or sensed images. Image registration involves spatially transforming the source/moving image(s) to align with the target image.
Other uses include seamless image stitching, [5] removal of unwanted details from an image, [1] non-photorealistic rendering filters, [2] image deblocking, [2] the ability to seamlessly clone one part of an image onto another in ways that are difficult to achieve with conventional image-domain techniques, [1] and high-dynamic-range imaging [6 ...
combine overlapping images for panoramic photography; correct complete panorama images, e.g. those that are "wavy" due to a badly levelled panoramic camera; stitch large mosaics of images and photos, e.g. of long walls or large microscopy samples; find control points and optimize parameters with the help of software assistants/wizards
The RANSAC algorithm is often used in computer vision, e.g., to simultaneously solve the correspondence problem and estimate the fundamental matrix related to a pair of stereo cameras; see also: Structure from motion, scale-invariant feature transform, image stitching, rigid motion segmentation.