The construction of mosaic images and the use of such images on several computer . A natural domain for representing and compositing images acquired by a camera . Technical Report CRL 97/4, Digital Equipment Corp. compositing images is presented. ‘stitch’ a sequence of digital images, and then composite . mosaic into disjoint regions leads to a compositing method. to produce seamless and smooth mosaics from random sequences of digital aerial im- ages and Image mosaicking consists of compositing a col- lection of .
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A detailed discussion on spatial transformation and interpolation methods can be found in [ 45 ].
AN INTRODUCTION TO IMAGE MOSAICING
The complete representation of static scenes resulting from mosaicing video frames in conjunction with an efficient representation for dynamic changes provide a versatile environment for visualizing, digtial coding, accessing, analyzing information. Smoother results can be obtained by a nonlinear transformation [ 48 ]. The input images are referred to as the foreground image and the background image.
These family of strips can handle a wide variety of motions including forward motion and optical zoom. Global transformations described above impose a single mapping function on the image. Although the local transformations can correct deformations that are not corrected by global corrections it is difficult to justify their necessity in image mosaicing.
We expect the use of image mosaicing to make a significant impact in video processing [ 23 ]. Using images acquired with a fish-eye lens [ 12 ] and the small relative size of polar regions with respect to composting images alleviates the negative effect of singularities. When many partially transparent layers need to be composited together, it is worthwhile to consider the algebraic properties of compositing operators used.
For compositing operators that are commutativesuch as additive blendingit is safe to re-order the blending operations. Improvements in computer technology became a natural motivation to develop computational techniques and to solve related problems.
There are complsiting radically different digital compositing workflows: It also spreads the error equally. Stereographic transformations are capable of mapping a full field of view of the viewing sphere onto the projection plane.
If the transformation is a bilinear transformation i. More generally, nonlinear compositing can have effects such as “halos” around composited objects, because the influence of the alpha channel is non-linear.
Perspective transformations preserve lines whereas the stereographic transformations preserve circular shapes [ 29 ]. It has been a central issue for a variety of problems in image processing [ 51 ] such as object recognition, monitoring satellite images, matching compositting images for reconstructing depth, matching biomedical images for diagnosis, etc.
Eliminating seams from image mosaics.
Finding the best separation border between overlapping images [ 57 ] has the potential to eliminate remaining geometric distortions. The order of the transformation increases as the number of points that need to be matched is increased. As indicated by the recent history of newly developed applications, image mosaicing has become a major field of research.
As opposed to homography techniques which project images to a reference frame e. Such a border is likely to traverse around moving objects avoiding double exposure [ 5630 ].
Additional formulation is developed in [ 42 ] for these complicated cases of motion. Note that this expression indicates compositing C with all of the layers below it in one step and then blending all of the layers on top of it with the previous result to produce the final image in the second step. It is possible for a compositing artist to compensate for the effects of compositing in non-linear space.
Compositing is performed by mathematically combining information from the corresponding pixels from the two input images and recording the result in a third image, which is called the composited image. The vector t is the translation component of the above equation.
Mosaicing images on smooth surfaces e.
Images acquired from hill-tops or balloons were manually pieced together. Some of the most common global transformations are affine, perspective and mosaice transformations. Due to the limitations of processing speed and memory, compositing artists did not usually have the luxury of having the system make intermediate conversions to linear space for the compositing steps.
Iteratively adjusting camera-motion parameters leads to local minimums unless a reliable initial estimate is provided.
Relative rotational motions between image pairs are used in [ 13 ] based on quaternions [ 34 ] and [ 11 ] based on an angular motion matrix [ 35 ] before mapping images onto a sphere to avoid the effect of singularities in registration. An example of this exists in the Adobe program After Effects.