Download Computer vision for visual effects by Richard J. Radke PDF

By Richard J. Radke

"Modern blockbuster videos seamlessly introduce most unlikely characters and motion into real-world settings utilizing electronic visible results. those results are made attainable through learn from the sphere of laptop imaginative and prescient, the examine of ways to instantly comprehend photos. machine imaginative and prescient for visible results will train scholars, engineers and researchers concerning the primary machine imaginative and prescient rules and state of the art algorithms used to create state-of-the-art visible results for videos and tv. the writer describes classical computing device imaginative and prescient algorithms used regularly in Hollywood (such as blue display matting, constitution from movement, optical circulation and have monitoring) and intriguing fresh advancements that shape the foundation for destiny results (such as average photograph matting, multi-image compositing, picture retargeting and look at synthesis). He additionally discusses the applied sciences in the back of movement catch and three-d information acquisition. greater than 2 hundred unique pictures demonstrating ideas, algorithms and effects, besides in-depth interviews with Hollywood visible results artists, tie the mathematical ideas to real-world filmmaking"-- learn more... desktop generated contents be aware: 1. advent; 2. snapshot matting; three. photo compositing and modifying; four. beneficial properties and matching; five. Dense correspondence and its purposes; 6. Matchmoving; 7. movement seize; eight. three-d information acquisition; Appendix: optimization algorithms for computing device imaginative and prescient

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405] were the first to extend graph-cut segmentation to the matting problem. 21b). 21c). , a garbage matte). Everything outside the box is assumed to be background with α = 0, and everything inside the box is assumed to be unknown, with an initial estimate of α = 1. As in Bayesian matting, initial Gaussian mixture models are fit to the foreground and background intensities inside and outside the box. The GrabCut algorithm iterates three steps until the binary α labels have converged: 1. 2. 3. Each pixel is assigned to one of the foreground (if αi = 1) or background (if αi = 0) Gaussian mixture components.

98] and extended to work in real-world environments by Wexler et al. [545]. 2 Matting with Custom Hardware Finally, we note that additional, customized hardware can greatly improve the ease and quality of pulling a matte. Such methods have the advantage of not requiring user input like a trimap or scribbles, but have disadvantages in terms of generalizability, expense, and calibration effort. For example, early work on matting in Hollywood used sodium lighting to create a yellowish background of a frequency that could be filtered from color film and used to expose a registered strip of matte film, removing the need to unmix colors [393, 517].

This matrix plays a key role in the rest of the chapter. 36) where µk and k are the mean and covariance matrix of the colors in window k and δij is the Kronecker delta. Frequently, the windows are taken to be 3 × 3, so W = 9. 11). Chapter 2. 38) is sometimes called the matting affinity. 35) we can see that minimizing J (α) corresponds to solving the linear system Lα = 0. That is, we must simply find a vector in the nullspace of L. 3 Constraining the Matte However, so far we haven’t taken into account any user-supplied knowledge of where the matte values are known; without this knowledge, the solution is ambiguous; for example, it turns out that any constant α matte is in the nullspace of L.

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