What is the Truth: A Survey of Video Compositing Techniques <s> B. Compositing of Real Footages <s> Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system. <s> BIB001 </s> What is the Truth: A Survey of Video Compositing Techniques <s> B. Compositing of Real Footages <s> No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments. > <s> BIB002 </s> What is the Truth: A Survey of Video Compositing Techniques <s> B. Compositing of Real Footages <s> Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection. <s> BIB003
For compositing real to real footages, chroma-keying is performed. In the first experiment, footages of an actor are merged with other footages for some people in Alexandria, Egypt, as shown in Fig. 13 (a) . First, the actor was recorded at a green screen studio using a static camera. The green screen footages were aligned with the layer of the other footages using layer-based compositing technique; see Fig. 13 (b) . Chroma-keying using "keylight" effect that is presented by Adobe After Effects was performed, as shown in Fig. 13 (c) . KLT technique BIB001  BIB002 was used for tracking the train. We chose the train in the footages of the people, because the train is one of the static objects in the footages. The tracking process was performed for applying the tilt/pan camera movements to the layer of the actor. Color correction using matching black and white point technique  is shown in Fig 13 (d). Fig 13 (e) shows the quality matching process that was performed using median cut algorithm  . Where, the color quantization of the actor's layer is applied for matching the quality of the layer of the actor and the second layer. There is an occluded person by the actor, as shown in the fourth frame in Fig. 13 . For more realistic result, this occluded person was tracked using mocha tracking software for separating him in a layer that occludes the actor's layer; see Fig. 13 (f) . Another example is shown in Fig. 14 , where actors were separated from the studio using chroma-keying to be merged with other footage. The two actors were recorded with a green screen, as shown in Fig. 14 (a) , to be integrated with other footage, as shown in Fig. 14 (b) . Fig. 14 (c-d) show two frames after the color correction process. Because of camera movement in the second footages, the tracking process was performed as the previous experiment. To get a stable marker for the tracking process, a visual black and white marker, that contains 12 corners, was used. See the red rectangle in Fig. 14 (e) . For removing this marker, the marker was tracked using mocha tracker, and then a clear part of the table without any marker was used by Poisson image editing technique BIB003 as a source region to be cloned with the table in the original footages. 