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

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

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

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

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

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

Making the experience of e-learning more effective requires interactive and collaborative systems to be adaptive and customizable. Specialized learning systems tend to be monolithic and difficult to extend. We present an alternative approach, where we synthesize a customizable learning environment from existing tools (Trac, SVN, reST, SQLite). The system presents the student not just with content, but an immersive experience that allows both individual and group annotations, versioning of the student's work, custom querying, and a uniform markup language to store content. We report the motivation and design of such an environment. We demonstrate the use of this system and its ability to plug into other environments by showcasing a custom interactive workbook, built for teaching and learning the principles of programming.

Abstract

In this work the authors propose a data coding protocol that leads to power reduction for block data transfer in off-chip buses. I/O pads driving off-chip buses contribute to a major portion of power dissipation in chips. Also, block data transfer is preferred in most systems like caches, DMA etc. In this proposed work, the prior knowledge of the block of data to be transmitted, when it is stored in the buffer, is exploited in a serial fashion to reduce transitions on every bus line. Statistical analysis shows up to 31.9% reduction in transitions. Benchmark results show that it leads to 29% reduction in power consumption. The technique provides added error detection on the lines of parity bit technique, with similar average error detection capability.

Abstract

In this work, the authors present the idea of inexact decision making, and its application to threshold voting of Hamming weights, used in bus coding schemes, N-modular redundancy (NMR), median filtering, and other pattern matching applications. Decision circuits can be tweaked, to perform in an inexact manner, in order to optimize in terms of delay and power, but still maintaining high system accuracy. One such circuit identified by the authors is the threshold voting of Hamming weights. A majority voter is a special case of this family of circuits. The proposed inexact voter consumes up to 8 times less power than the exact voter, with negligible reduction in system accuracy and performance. The leakage power is also reduced by a factor of 3. The inexact voter allows for a higher frequency of operation, by reducing the critical path delay by a factor of up to 3.4. The results obtained validate the application of inexact decision making, with respect to threshold voters.

Abstract

Here in this paper we present a snake robot which can autonomously change its gait depending on the terrain. Also we present the design and construction details for the same. Here a custom simulator for the robot and an API for the same was developed which reduced the design and development time. For controlling the Snake robot manually a data glove using accelerometers was developed. The Snake robot can also be operated in autonomous mode where an over camera is used for sensing the terrain and the snake autonomously travels from one part of the terrain to another by switching its gait