Graphics Multimedia

Computer vision for visual effects by Richard J. Radke

By Richard J. Radke

Smooth blockbuster video clips seamlessly introduce very unlikely characters and motion into real-world settings utilizing electronic visible results. those results are made attainable through examine from the sphere of desktop imaginative and prescient, the research of ways to immediately comprehend pictures. computing device imaginative and prescient for visible results will teach scholars, engineers, and researchers concerning the basic laptop imaginative and prescient ideas and cutting-edge algorithms used to create state of the art visible results for videos and tv. the writer describes classical machine imaginative and prescient algorithms used frequently in Hollywood (such as blue monitor matting, constitution from movement, optical stream, and have monitoring) and intriguing fresh advancements that shape the foundation for destiny results (such as common snapshot matting, multi-image compositing, picture retargeting, and look at synthesis). He additionally discusses the applied sciences at the back of movement catch and three-d facts acquisition. greater than two hundred unique photos demonstrating rules, algorithms, and effects, in addition to in-depth interviews with Hollywood visible results artists, tie the mathematical techniques to real-world filmmaking.

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5 with uncertainty 1. The algorithm proceeds by moving pixels from U that are within a certain radius of a K pixel to K (this is like a “rind” of the unlabeled region). Then values for F , B, α, and u are estimated for each pixel in K. The process iterates until U is empty and the uncertainties in K stop decreasing. 54), where V is the set of pixels currently in K. 2. The LBP algorithm assumes that α can take on one of K discrete states between 0 and 1. 2), but the Edata term requires a little more explanation.

My experience was that academic papers sometimes used very complicated models that tend to work very well for some datasets and extremely poorly on other datasets, whereas the film industry tends to split the difference and go for something that works reliably most of the time. Another big issue with the film industry is that you’re always working on video. A lot of these single-image techniques work really well given user input, but getting something to work well on video is incredibly hard because you need that temporal consistency.

The defocus matting problem is over-constrained (nine equations in the seven unknowns of the matting equation) and the matte can be computed using the approximate depth of the foreground and the three cameras’ optical parameters. The system was extended by Joshi et al. [227] so that one camera could have a different center of projection from the others. McGuire et al. , a camera with two imaging sensors that share the same optical axis) and a special diffuse-gray screen. 2). Finally, Joshi et al.

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