In this paper we present a set of efficient image based rendering methods capable of rendering multiple frames per second on a PC. The first method warps Sprites with Depth representing smooth surfaces without the gaps found in other techniques. A second method for more general scenes performs warping from an intermediate representation called a Layered Depth Image (LDI). An LDI is a view of the scene from a single input camera view, but with multiple pixels along each line of sight. The size of the representation grows only linearly with the observed depth complexity in the scene. Moreover, because the LDI data are represented in a single image coordinate system, McMillan's warp ordering algorithm can be successfully adapted. As a result, pixels are drawn in the output image in backto-front order. No z-buffer is required, so alpha-compositing can be done efficiently without depth sorting. This makes splatting an efficient solution to the resampling problem.
The Web abounds with dyadic data that keeps increasing by every single second. Previous work has repeatedly shown the usefulness of extracting the interaction structure inside dyadic data [21,9,8]. A commonly used tool in extracting the underlying structure is the matrix factorization, whose fame was further boosted in the Netflix challenge [26]. When we were trying to replicate the same success on real-world Web dyadic data, we were seriously challenged by the scalability of available tools. We therefore in this paper report our efforts on scaling up the nonnegative matrix factorization (NMF) technique. We show that by carefully partitioning the data and arranging the computations to maximize data locality and parallelism, factorizing a tens of millions by hundreds of millions matrix with billions of nonzero cells can be accomplished within tens of hours. This result effectively assures practitioners of the scalability of NMF on Web-scale dyadic data.
As streaming audio-video technology becomes widespread, there is a dramatic increase in the amount of multimedia content available on the net. Users face a new challenge: How to examine large amounts of multimedia content quickly. One technique that can enable quick overview of multimedia is video summaries; that is, a shorter version assembled by picking important segments from the original. We evaluate three techniques for automatic creation of summaries for online audio-video presentations. These techniques exploit information in the audio signal (e.g., pitch and pause information), knowledge of slide transition points in the presentation, and information about access patterns of previous users. We report a user study that compares automatically generated summaries that are 20%-25% the length of full presentations to author generated summaries. Users learn from the computer-generated summaries, although less than from authors' summaries. They initially find computer-generated summaries less coherent, but quickly grow accustomed to them.
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