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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.
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 ...
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
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 ...
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.
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 .
This is capture of optically coded images, followed by computational decoding to produce new images. Coded aperture imaging was mainly applied in astronomy or X-ray imaging to boost the image quality. Instead of a single pin-hole, a pinhole pattern is applied in imaging, and deconvolution is performed to recover the image. [9]
Z-fighting, also called stitching or planefighting, is a phenomenon in 3D rendering that occurs when two or more primitives have very similar distances to the camera. This would cause them to have near-similar or identical values in the z-buffer , which keeps track of depth.