By Richard J. Radke
Sleek blockbuster video clips seamlessly introduce very unlikely characters and motion into real-world settings utilizing electronic visible results. those results are made attainable via learn from the sphere of machine imaginative and prescient, the learn of ways to immediately comprehend photographs. desktop imaginative and prescient for visible results will teach scholars, engineers, and researchers concerning the primary computing device imaginative and prescient ideas and state of the art 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 movement, and have monitoring) and interesting contemporary advancements that shape the root for destiny results (such as average photo matting, multi-image compositing, photograph retargeting, and consider synthesis). He additionally discusses the applied sciences at the back of movement trap and third-dimensional facts acquisition. greater than two hundred unique photos demonstrating ideas, algorithms, and effects, in addition to in-depth interviews with Hollywood visible results artists, tie the mathematical strategies to real-world filmmaking.
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Extra resources for Computer vision for visual effects
We will discuss algorithms that exploit this coherence in the rest of this chapter. , that the samples formed ellipsoidal clusters in color space). However, it turns out that in many natural images, the foreground and background distributions look more like lines or skinny cigar shapes . 4. 9 — the ﬁtted Gaussians are generally long and skinny. Levin et al.  exploited this observation in an elegant algorithm called closed-form matting. 1 The Color Line Assumption Let’s assume that within a small window wj around each pixel j, the sets of foreground and background intensities each lie on a straight line in RGB space.
8 HARD-SEGMENTATION-BASED MATTING It’s important to understand the relationship between the matting problem and image segmentation. The key difference is that the goal of segmentation is to decompose an image into disjoint pieces that ﬁt together to form a whole. In traditional segmentation, the edges of the pieces are hard, not fuzzy, and a segmentation can be deﬁned by an integer label for every pixel in the image. In the case where only two pieces are desired, that is, foreground and background, we can label the pieces by 1 and 0 respectively and think of a segmentation as a coarse matting problem with no fractional α values.
1 to video. 8, but the nodes in the graph (here, image superpixels) are connected both in space and time, with inter-frame edge weights estimated similarly to intra-frame edge weights. Criminisi et al.  also posed video segmentation as a conditional random ﬁeld energy minimized with graph cuts, but added an explicit learned prior on the foreground likelihood at a pixel based on its label in the previous two frames. 21b-c). Wang et al.  also proposed a graph-cut-based video segmentation method, but extended the superpixel formation, user stroking, and border matting algorithms to operate natively in the space-time “video volume” formed by stacking the frames at each time instant.