DIGITAL COMPOSITING MOSAICS PDF

Combine all the images taken at a wedding and match them with the best picture of the bride and groom. Or create a mosaic of all of the employees at a company and overlay them over an image of the main product or service that they offer. A Beautiful Way to Summarize and Event or an Idea All you need to make this effect work is a lot of photos and strong theme. The theme can simply be built around where all of the images were taken think an event, like a wedding or concert.

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Shortly after the photographic process was developed in , the use of photographs was demonstrated on topographical mapping [ 1 ]. Images acquired from hill-tops or balloons were manually pieced together. After the development of airplane technology aerophotography became an exciting new field.

The limited flying heights of the early airplanes and the need for large photo-maps, forced imaging experts to construct mosaic images from overlapping photographs. This was initially done by manually mosaicing [ 2 ] images which were acquired by calibrated equipment.

The need for mosaicing continued to increase later in history as satellites started sending pictures back to earth. Improvements in computer technology became a natural motivation to develop computational techniques and to solve related problems. There have been a variety of new additions to the classic applications mentioned above that primarily aim to enhance image resolution and field of view.

Image-based rendering [ 3 ] has become a major focus of attention combining two complementary fields: computer vision and computer graphics [ 4 ]. In computer graphics applications e. These images are used as static background of synthetic scenes and mapped as shadows onto synthetic objects for a realistic look with computations which are much more efficient than ray tracing [ 6 , 7 ].

In early applications such environment maps were single images captured by fish-eye lenses or a sequence of images captured by wide-angle rectilinear lenses used as faces of a cube [ 5 ]. Mosaicing images on smooth surfaces e. Such immersive environments with or without synthetic objects provide the users an improved sense of presence in a virtual scene. A combination of such scenes used as nodes[ 8 , 13 ] allows the users to navigate through a remote environment.

Computer vision methods can be used to generate intermediate views [ 14 , 9 ] between the nodes. As a reverse problem the 3D stucture of scenes can be reconstructed from multiple nodes [ 15 , 16 , 13 , 17 , 18 ].

Among other major applications of image mosaicing in computer vision are image stabilization [ 19 , 20 ], resolution enhancement [ 21 , 22 ], video processing [ 23 ] e. Eliminating seams from image mosaics. Transformations can be global or local in nature. Global transformations are usually defined by a single equation which is applied to the whole image. Local transformations are applied to a part of image and they are harder to express concisely.

Figure 1: Common geometric transformations. Some of the most common global transformations are affine, perspective and polynomial transformations. The first three cases of the Fig 1 are typical examples for the affine transformations. The remaining two are the common cases where perspective and polynomial transformations are used, respectively.

Alternatively, perspective transformations are often represented by the following equations known as homographies: Eight unknown parameters can be solved without any 3D information using only correspondences of image points 1.

The point correspondences can be obtained by feature based methods e. Note that the transformation found for corresponding images is globally valid for all image points only when there is no motion parallax between frames e.

The motion parameters can also be found iteratively e. The 8-parameter homography accurately models a perspective transformation between different views for the case of a camera rotating around a nodal point. Such a perspective transformation is shown in Fig 2. Fig 2 also illustrates some of the projective transformations that are alternative to the perspective transformation.

Each of these projective transformations has distinctive features. Perspective transformations preserve lines whereas the stereographic transformations preserve circular shapes [ 29 ].

Stereographic transformations are capable of mapping a full field of view of the viewing sphere onto the projection plane. For the equi-distant projection which can be viewed as flattening a spherical surface [ 30 ] mapping a full field of view is no longer an asymptotical case.

As opposed to homography techniques which project images to a reference frame e. We utilize this versatile camera model in [ 30 , 31 ]. A plenoptic function describes everything that is visible in radiant forms of energy to an observer for every possible location of the observer. A plenoptic image 2 is a sample of the plenoptic function for a fixed location of the observer.

Side views of cylindrical maps [ 8 , 9 , 10 ] are often chosen to represent plenoptic images compromising the discarded views of top and bottom with the uniform sampling in the cylindrical coordinate system.

Uniform sampling feature is desirable especially when images are needed to be translated in the target domain. We use spherical surfaces as in [ 11 , 12 , 13 ] as an environment to construct plenoptic images. The construction of mosaic images on spherical surfaces is complicated by the singularities at the poles [ 33 ]. Numerical errors near the poles cause irregularities in the similarity measures used for automatic registration.

Using images acquired with a fish-eye lens [ 12 ] and the small relative size of polar regions with respect to such images alleviates the negative effect of singularities. 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.

Images that form a large portion of a plenoptic image can be constructed on a single image frame by using special mirrors [ 36 , 37 , 38 ].

Having a single viewpoint [ 36 , 37 ] in such imaging systems is important for capability to reconstruct perspective views.

Carefully calibrated and coupled mirrors [ 37 ] can capture two images that can be easily combined to form a plenoptic image. Even though this kind of approach provides a simple framework for capturing a full field of view of a scene, the limited resolution of the film frame or sensor array may be a serious limitation for recording images in detail. Plenoptic images constructed by mosaicing smaller images can store detailed information without being subject to such limits.

This action can be emulated using conventional cameras by combining strips taken from a sequence of two dimensional images as a series of neighboring segments. These cameras can directly acquire cylindrical with a rotating motion and orthographic with translational motion maps [ 39 ]. They can also acquire images along an arbitrary path [ 40 ]. The strips that should be taken from two dimensional images are identified as the ones perpendicular to the image flow in [ 41 ].

These family of strips can handle a wide variety of motions including forward motion and optical zoom. Additional formulation is developed in [ 42 ] for these complicated cases of motion. Images acquired as a combination of strips along with range images and a recorded path of camera are also shown to be effective in complete 3D reconstruction of scenes [ 43 ].

Using only correspondences of image points they can handle global distortions e. A bivariate polynomial transformation is of the form: The order of the transformation increases as the number of points that need to be matched is increased. If the transformation is a bilinear transformation i. If the order of the polynomial is not high enough to solve with direct matrix inversion, a pseudo inverse solution can be obtained.

This solution gives identical results with the classical least squares formulation which yield those coefficients that best approximate the true mapping function for control points.

It also spreads the error equally. Weighted least squares solutions [ 45 ] introduce a weighting function which localizes the error. Irani et al. They use the extra degrees of freedom in the transformation to deal with the nonlinearities due to parallax, scene change etc.

Global transformations described above impose a single mapping function on the image. They do not account for with the exception of weighted least squares solution local variations. Local distortions may be present in scenes due to a motion parallax, movement of objects etc. The parameters of a local mapping transformation vary across the different regions of the image to handle local deformations. One way to do this is to partition the image into smaller sub-regions such as triangular regions with corners at the control points and then find a linear [ 47 ] transformation that exactly maps corners to desired locations.

Smoother results can be obtained by a nonlinear transformation [ 48 ]. In [ 49 ] the control points are selected to be along the desired border of overlapping images. A transformation that relocates these points to align with their correspondences has an effect on rest of the pixels inversely proportional to their distances to the control points. In [ 50 ] local variations that need to be corrected are estimated by the image flow between corresponding images that have undergone global transformations.

Although the local transformations can correct deformations that are not corrected by global corrections it is difficult to justify their necessity in image mosaicing. Warping images simply to reduce local variations e.

We address the problem of local distortions during the mosaicing process by minimizing their significance in the blended images. A detailed discussion on spatial transformation and interpolation methods can be found in [ 45 ].

It has been a central issue for a variety of problems in image processing [ 51 ] such as object recognition, monitoring satellite images, matching stereo images for reconstructing depth, matching biomedical images for diagnosis, etc.

Registration is also the central task of image mosaicing procedures. Carefully calibrated and prerecorded camera parameters may be used to eliminate the need for an automatic registration.

User interaction also is a reliable source for manually registering images e. Automated methods for image registration used in image mosaicing literature can be categorized as follows: Feature based [ 52 , 27 ] methods rely on accurate detection of image features.

Correspondences between features lead to computation of the camera motion which can be tested for alignment. In the absence of distinctive features, this kind of approach is likely to fail. Exhaustively searching for a best match for all possible motion parameters can be computationally extremely expensive. Using hierarchical processing i. We also use this approach also taking advantage of parallel processing [ 31 ] for additional performance improvement.

These methods also require the overlap extent to occupy a significant portion of the images e. Iteratively adjusting camera-motion parameters leads to local minimums unless a reliable initial estimate is provided. Initial estimates can be obtained using a coarse global search or an efficiently implemented frequency domain approach [ 28 , 18 ].

Alignment of images may be imperfect due to registration errors resulting from incompatible model assumptions, dynamic scenes, etc. These unwanted effects can be alleviated during the compositing process. The main problem in image compositing is the problem of determining how the pixels in an overlapping area should be represented. Finding the best separation border between overlapping images [ 57 ] has the potential to eliminate remaining geometric distortions.

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Voodooshakar Learn how and when to remove these template messages. In this case we may compute S: These unwanted effects can be alleviated during the compositing process. If this operation has to be done in real time dkgital games there is an easy trick to boost performance. The need for mosaicing continued to increase later in history as satellites started sending pictures back to earth. Carefully calibrated and prerecorded camera parameters may be used to eliminate the need for an automatic registration.

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DIGITAL COMPOSITING MOSAICS PDF

Shortly after the photographic process was developed in , the use of photographs was demonstrated on topographical mapping [ 1 ]. Images acquired from hill-tops or balloons were manually pieced together. After the development of airplane technology aerophotography became an exciting new field. The limited flying heights of the early airplanes and the need for large photo-maps, forced imaging experts to construct mosaic images from overlapping photographs. This was initially done by manually mosaicing [ 2 ] images which were acquired by calibrated equipment.

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Kagam Specifically, the associativity and commutativity determine when repeated calculation can or cannot be avoided. Images acquired from hill-tops or balloons were manually pieced together. In computer graphics applications e. To form F with an associative operator, we need only do two additional compositing operations to integrate the new layer S, by computing F: Eight unknown parameters can be solved without any 3D information using only correspondences of image points 1. The affine transformation can be represented by a single matrix multiplication in homogenous coordinates: Without any special considerations, four full-image blends would need to occur. In [ 49 ] the control points are selected to be along the desired border of overlapping images.

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Dutilar 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. Feature based [ ] methods rely on accurate detection of image features. Weighted least squares solutions [ 45 ] introduce a weighting function which digitzl the error. Digital compositing is the process of digitally assembling multiple images to make a final image, typically for print, motion pictures or screen display. This page was last edited on 26 Septemberat Transformations can be global or local in nature. If composihing layers of an image change regularly but a large number of layer still need to be composited such as in distributed renderingthe commutativity of a compositing operator can still be exploited to speed up computation through parallelism even when there is no gain from pre-computation. The complete representation of static scenes resulting from mosaicing video frames in conjunction dihital an efficient representation for dynamic changes provide a versatile environment for visualizing, efficiently coding, accessing, analyzing information.

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