Saliency Guided Summarization of Molecular Dynamics Simulations

We present a novel method to measure saliency in molecular dynamics simulation data. This saliency measure is based on a multiscale center-surround mechanism, which is fast and efficient to compute. We explore the use of the saliency function to guide the selection of representative and anomalous timesteps for summarization of simulations. To this end, we also introduce a multiscale keyframe selection procedure which automatically provides keyframes representing the simulation at varying levels of coarseness. We compare our saliency guided keyframe approach against other methods, and show that it consistently selects superior keyframes as measured by their predictive power in reconstructing the simulation

VIINTER: View Interpolation with Implicit Neural Representations of Images

We present VIINTER, a method for view interpolation by interpolating the implicit neural representation (INR) of the captured images. We leverage the learned code vector associated with each image and interpolate between these codes to achieve viewpoint transitions. We propose several techniques that significantly enhance the interpolation quality. VIINTER signifies a new way to achieve view interpolation without constructing 3D structure, estimating camera poses, or computing pixel correspondence. We validate the effectiveness of VIINTER on several multi-view scenes with different types of camera layout and scene composition. As the development of INR of images (as opposed to surface or volume) has centered around tasks like image fitting and super-resolution, with VIINTER, we show its capability for view interpolation and offer a promising outlook on using INR for image manipulation tasks.Comment: SIGGRAPH Asia 202

High-throughput sequence alignment using Graphics Processing Units

<p>Abstract</p> <p>Background</p> <p>The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and <it>de novo </it>genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies.</p> <p>Results</p> <p>This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies.</p> <p>Conclusion</p> <p>MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU.</p

A Hierarchy of Techniques for Simplifying Polygonal Models

Introduction It is evident from these course notes and their references that there is a tremendous interest in the general area of simplification of polygonal objects. There are several ways in which one can classify the various simplification techniques. One of these is by examining the nature of the performed simplifications. This section of the course will overview some representative techniques from a hierarchy of increasingly aggressive simplification techniques. In Section 2 we overview a popular approach to compress the connectivity information for polygonal objects -- by using triangle strips. Section 3 overviews Simplification Envelopes and their features. In Section 4 we overview some of our research in simplifying the genus of an object. Constructing a multiresolution hierarchy is only part of the solution in a level-of-detail-based rendering scheme. Switching levels of detail among different objects and at different regions within the same object i

Global Contours

Figure (b) and the salient contours shown in Figure (c) are calculated by Vonikakis et al. [2006]. Figure (d) shows the image saliency computed by the method of Itti et al. [1998]. In this image, salient regions are marked by yellow circles. Image saliency tends to focus on the local changes. Figure (e) shows the Global Contours calculated by our method. The darker regions in the contour lines are considered more salient. As we perform global computation, our calculated contours tend to reflect the most dominant property of the image. We present a multi-scale approach that uses Laplacian eigenvectors to extract globally significant contours from an image. The input images are mapped into the Laplacian space by using Laplacian eigenvectors. This mapping causes globally significant pixels along the contours to expand in the Laplacian space. The measure of the expansion is used to compute the Global Contours. We apply our scheme to real color images and compare it with several other methods that compute image and color saliency. The contours calculated by our method reflect global properties of the image and are complementary to classic center-surround image saliency methods. We believe that hybrid image saliency algorithms that combine our method of Global Contours with center-surround image saliency algorithms will be able to better characterize the most important regions of images than those from just using contours calculated using bottom-up approaches. Laplacian eigenmaps, saliency, contours, global fea-Keywords: tur