Abstract

The rapid release model of software introduces frequent updates to the existing software every twelve-eighteen weeks, forcing a user to get accustomed to its new features. We propose an experimental study to compare the learning gaps that are introduced when a user is exposed to a new version of a software, with which he is previously acquainted. In order to explore the problem, we propose four models of a machine, with each model involving an update either to the functionality, or to the user interface, or both. We conducted a between-subjects experimental study with thirty-two participants who performed two tasks successively on two models of a machine, the second one being a updated model of the first. The analysis of the data using ANOVA implies that a change in the user interface dominates a change in the functionality. Results indicate that 88% of the errors were caused due to a change in the user interface. 87.5% of the users who underwent a change in the user interface hold this change responsible for the learning gap, while only 56.25% users who underwent a change in the functionality consider it to be a potential reason for the learning gap.

Abstract

The astonishing diversity in folding patterns of RNA three-dimensional (3D) structures is crafted by myriads of noncovalent contacts, of which base pairing and stacking are the most prominent. A systematic and comprehensive classification and annotation of these interactions is necessary for a molecular-level understanding of their roles. However, unlike in the case of base pairing, where a widely accepted nomenclature and classification scheme exists in the public domain, currently available classification schemes for base–base stacking need major enhancements to comprehensively capture the necessary features underlying the rich stacking diversity in RNA. Here, we extend the previous stacking classification based on nucleobase interacting faces by introducing a structurally intuitive geometry-cum topology-based scheme. Specifically, a stack is first classified in terms of the geometry described by the relative orientation of the glycosidic bonds, which generates eight basic stacking geometric families for heterodimeric stacks and six of those for homodimeric stacks. Further annotation in terms of the identity of the bases and the region of involvement of purines (five-membered, six-membered, or both rings) leads to the enumeration of 384 distinct RNA base stacks. Based on our classification scheme, we present an algorithm for automated identification of stacks in RNA crystal structures and analyze the stacking context in selected RNA structures. Overall, the work described here is expected to greatly facilitate the structure-based RNA research.

Abstract

The AXES project participated in the interactive known-item search task (KIS) and the interactive instance search task (INS) for TRECVid 2011. We used the same system architecture and a nearly identical user interface for both the KIS and INS tasks. Both systems made use of text search on ASR, visual concept detectors, and visual similarity search. The user experiments were carried out with media professionals and media students at the Netherlands Institute for Sound and Vision, with media professionals performing the KIS task and media students participating in the INS task. This paper describes the results and findings of our experiments.

Abstract

Advances in cameras and web technology have madeit easy to capture and share large amounts of video data over toa large number of people. A large number of cameras overseepublic and semi-public spaces today. These raise concerns on theunintentional and unwarranted invasion of the privacy of individ-uals caught in the videos. To address these concerns, automatedmethods tode-identifyindividuals in these videos are necessary.De-identification does not aim at destroying all informationinvolving the individuals. Its ideal goals are to obscure the identityof the actor without obscuring the action. This paper outlinesthe scenarios in which de-identification is required and the issuesbrought out by those. We also present an approach to de-identifyindividuals from videos. Our approach involves tracking andsegmenting individuals in a conservative voxel space involvingx, y,and time. A de-identification transformation is applied perframe using these voxels to obscure the identity. Face, silhouette,gait, and other characteristics need to be obscured, ideally. Weshow results of our scheme on a number of videos and forseveral variations of the transformations. We present the resultsof applying algorithmic identification on the transformed videos.We also present the results of a user-study to evaluate how wellhumans can identify individuals from the transformed videos

Abstract

Raytracing dynamic scenes at interactive rates have received a lot of attention recently. We present a few strategies forhigh performance raytracing on a commodity GPU. The construction of grids needs sorting, which is fast on today’s GPUs. The grid isthus the acceleration structure of choice for dynamic scenes as per-frame rebuilding is required. We advocate the use of appropriatedata structures for each stage of raytracing, resulting in multiple structure building per frame. A perspective grid built for the cameraachieves perfect coherence for primary rays. A perspective grid built with respect to each light source provides the best performancefor shadow rays. Spherical grids handle lights positioned inside the model space and handle spotlights. Uniform grids are best forreflection and refraction rays with little coherence. We propose an Enforced Coherence method to bring coherence to them byrearranging the ray to voxel mapping using sorting. This gives the best performance on GPUs with only user-managed caches. Wealso propose a simple, Independent Voxel Walk method, which performs best by taking advantage of the L1 and L2 caches on recentGPUs. We achieve over 10 fps of total rendering on the Conference model with one light source and one reflection bounce, whilerebuilding the data structure for each stage. Ideas presented here are likely to give high performance on the future GPUs as well asother manycore architectures

Abstract

We propose an object centric representation for easy and in-tuitive navigation and manipulation of videos. Object centricrepresentation allows a user to directly access and process ob-jects as basic video components. We demonstrate a trajectorybased interface and example operations, which allow users toretime, reorder, remove or clone video objects in a ‘click anddrag’ fashion. This interface is created by extracting objectmotion information from the video. We use object detectionand tracking to obtain spatiotemporal video object tube. Thecorresponding object motion trajectories are represented in a3D(x,y,t)grid. Users can navigate and manipulate videoobjects by scrubbing or manipulating corresponding trajecto-ries. We show some example applications of proposed inter-face like object synchronization, saliency magnification, vi-sual effects and composite video creation.

Abstract

Video data is increasing rapidly along with the capacity of storage devices owned by a lay user. Users have mod­erate to large personal collections of videos and would like to keep them in an organized manner based on its content. Video organizing tools for personal users are way behind even the primitive image organizing tools. We present a mechanism in this paper to help ordinary users organize their personal collection of videos based on categories they choose. We cluster the PHOG features extracted from se­lected key frames to form a representation for each user­selected category during the learning phase. During the organization phase, labels from a K -NN classifier on these cluster centres for each key frame are aggregated to give a label to the video while categorizing. Video processing is computationally intensive. To peiform the computationally intensive steps involved, we exploit the CPU as well as the GPU that is common even on personal systems. Effective use of the parallel hardware on the system is the only way to make the tool scale reasonably to large collections that will be available soon. Our tool is able to organize a set of 100 sport videos of total duration of 1375 minutes in about 9.5 minutes. The process of learning the categories from 12 annotated videos of duration 165 minutes took 75 seconds on a GTX 580 card. These were on a standard desktop with an off-the-shelfGPU. The labeling accuracy is about 96% on all video

Abstract

s the complexity of 3D models used in computergraphics applications grows, there arises a need to visualize theoverall distribution of detail on them. Detail is a function ofthe amount of information present on a surface. In this paper,we present a method to quantify detail using a combination oflocal measures of curvature and density. We show that detailcan be used for applications like ordering for mesh decimation,visualizing abnormalities in a mesh and so on

Abstract

Visual surveillance is increasingly prevalent todaybut the privacy issues of individuals involved in surveillancevideos have not been dealt with adequately so far. In mostcases, if not all, the chief purpose of placing a camera can beserved without knowing the identity of the individuals involvedunless the activity is of some predetermined kind. One needsto transform these videos to protect identity of individualsinvolved possibly at the source camera itself. In this paperwe present the De-Identification Camera, which is a scalable,low cost and real-time solution to the privacy protection issues.Our main contribution lies in proposing a privacy protectionarchitecture which transforms the video at the camera level itself,We also present and implement a de-identification pipeline whichis suitable for real-time implementation. We implemented thesystem on a Texas Instrument OMAP4 based embedded platformand was able to de-identify videos in real time, transformingthe video on the camera itself ensures protection from variousattacks. We also address issues like data utility of surveillancevideos by making this solution customizable. We propose thatsuch systems can replace the traditional surveillance cameras inthe future by providing all the surveillance and privacy protectionsolutions on hardware, probably with few performance upgrades.

Abstract

Many image filtering operations provide ample par-allelism, but progressive non-linear processing of im-ages is among the hardest to parallelize due to long,sequential, and non-linear data dependency. A typicalexample of such an operation is error diffusion dither-ing, exemplified by the Floyd-Steinberg algorithm. Inthis paper, we present its parallelization on multicoreCPUs using a block-based approach and on the GPUusing a pixel based approach. We also present a hybridapproach in which the CPU and the GPU operatein parallel during the computation. High PerformanceComputing has traditionally been associated with highend CPUs and GPUs. Our focus is on everyday com-puters such as laptops and desktops, where significantcompute power is available on the GPU as on theCPU. Our implementation can dither an8K8Kimage on an off-the-shelf laptop with an Nvidia 8600MGPU in about 400 milliseconds when the sequentialimplementation on its CPU took about 4 seconds.