Computer vision for visual effects by Richard J. Radke

By Richard J. Radke

Sleek blockbuster videos seamlessly introduce most unlikely characters and motion into real-world settings utilizing electronic visible results. those results are made attainable by means of study from the sector of computing device imaginative and prescient, the examine of the way to immediately comprehend photos. laptop imaginative and prescient for visible results will train scholars, engineers, and researchers concerning the basic machine imaginative and prescient ideas and state of the art algorithms used to create state-of-the-art visible results for video clips and tv. the writer describes classical desktop imaginative and prescient algorithms used usually in Hollywood (such as blue monitor matting, constitution from movement, optical move, and have monitoring) and fascinating contemporary advancements that shape the root for destiny results (such as common snapshot matting, multi-image compositing, picture retargeting, and think about synthesis). He additionally discusses the applied sciences in the back of movement seize and 3-dimensional information acquisition. greater than two hundred unique photographs demonstrating ideas, algorithms, and effects, in addition to in-depth interviews with Hollywood visible results artists, tie the mathematical suggestions to real-world filmmaking.

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26) as a matrix equation:  N J (α, a, b) = j=1      j I1 .. j IW √ εI3×3 1 .. 1 0        aj bj   −   j α1 .. 4. C l o s e d - F o r m M a t t i n g 23 j j j j where W is the number of pixels in the window and {I1 , . . , IW } and {α1 , . . , αW } represent the ordered list of image colors and α values inside window j. 28) where α¯ j is a (W + 3) × 1 vector containing the α’s in window j followed by three 0’s. 29) That is, the optimal a and b in each window for a given matte α are linear functions of the α values.

49] showed how the parameter σ could be estimated based on the local contrast of an image sample. 20 illustrates a segmentation of an image from scribbles with this original graph-cut formulation. If the segmentation is incorrect in a subregion, new foreground/background scribbles can be added and the solution quickly updated without recomputing the minimum cut from scratch. , 0 for background and 1 for foreground). The edge weights between pixels and the foreground/background terminals make up the data energy term Edata and the inter-node weights make up the smoothness energy Esmoothness .

83) (i,j)∈C Boykov and colleagues showed that the globally optimal minimum cut could quickly be computed in low-order polynomial time [60], leading to an explosion of interest in graph-cut methods in the computer vision community. 3 gives more details on the basic algorithm. GPU-based [515] and multi-core [296] algorithms have been proposed to further accelerate finding the minimum cut. As with scribble-based matting, the user designates certain pixels to belong to the foreground F and others to the background B.

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