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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.
Image stitching is widely used in modern applications, such as the following: Document mosaicing [5] Image stabilization feature in camcorders that use frame-rate image alignment; High-resolution image mosaics in digital maps and satellite imagery; Medical imaging; Multiple-image super-resolution imaging; Video stitching [6] Object insertion
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 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 ...
SIFT features can essentially be applied to any task that requires identification of matching locations between images. Work has been done on applications such as recognition of particular object categories in 2D images, 3D reconstruction, motion tracking and segmentation, robot localization, image panorama stitching and epipolar calibration ...
It differs from some other image-stitching software in that it automatically and seamlessly stitches together even unaligned or zoomed photographs without user input, whereas others often require the user to highlight matching areas for the photographs to be merged properly. The only requirement is that all photographs be taken from a single point.
Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]
The correspondence problem, finding matches between two images so the position of the matched elements can then be triangulated in 3D space is the key issue here. Once you have the multiple depth maps you have to combine them to create a final mesh by calculating depth and projecting out of the camera – registration .