Computer vision for visual effects by Richard J. Radke

By Richard J. Radke

Glossy blockbuster video clips seamlessly introduce most unlikely characters and motion into real-world settings utilizing electronic visible results. those results are made attainable via examine from the sector of machine imaginative and prescient, the research of the way to immediately comprehend photos. laptop imaginative and prescient for visible results will teach scholars, engineers, and researchers concerning the primary desktop imaginative and prescient rules and state of the art algorithms used to create state of the art visible results for video clips and tv. the writer describes classical laptop imaginative and prescient algorithms used frequently in Hollywood (such as blue reveal matting, constitution from movement, optical circulate, and have monitoring) and fascinating contemporary advancements that shape the root for destiny results (such as usual photo matting, multi-image compositing, photo retargeting, and look at synthesis). He additionally discusses the applied sciences in the back of movement seize and three-d information acquisition. greater than two hundred unique pictures demonstrating rules, algorithms, and effects, in addition to in-depth interviews with Hollywood visible results artists, tie the mathematical innovations to real-world filmmaking.

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In this section, we overview techniques for video matting, which exploit the temporal coherence of the input video and desired output mattes. Just as single-image matting is related to image segmentation, video matting is related to the well-known problem of visual tracking. The goal of visual tracking is to estimate the location of one or more objects in a video sequence, preferably ensuring that the estimated locations vary smoothly and that tracking is not lost in the presence of occlusions or object crossings.

72) where L is the standard matting Laplacian and wF and wB are N ×1 vectors of terminal weights. 73) where WF and WB are N × N diagonal matrices with the vectors wF and wB on the diagonals, respectively. This objective function is quadratic in α and thus results in a slightly modified linear system from the one used in closed-form matting. Rhemann et al. 74) where αˆ is an N × 1 vector that acts as a prior estimate of α at every pixel in the matte, λ is a tunable parameter, and D is a diagonal matrix with the confidences ci on the diagonal.

1 Violations of the Matting Equation When a matte of an object needs to be created from an image containing strong directional lighting, the shadow of the object should intuitively be part of the foreground instead of part of the background. However, shadows don’t obey the matting equation, since they arise from a different method of image formation. 23). Chuang et al. 2). 10. M a t t i n g E x t e n s i o n s 43 video sequence from a fixed camera with fixed background and a moving foreground object, so that a “clean plate” background image without the shadow can be created.

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