tocauthor: Tang_N* : Human-Computer Interaction Bibliography Search

Pmomo: Projection Mapping on Movable 3D Object Real Reality Interfaces / Zhou, Yi / Xiao, Shuangjiu / Tang, Ning / Wei, Zhiyong / Chen, Xu Proceedings of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.1 p.781-790

ACM Digital Library Link

Summary: We introduce Pmomo (acronym of projection mapping on movable object), a dynamic projection mapping system that tracks the 6-DOF position of real-world object, and shades it with virtual 3D contents by projection. The system can precisely lock the projection on the moving object in real-time, even the one with complex geometry. Based on depth camera, we developed a novel and robust tracking method that samples the structure of the object into low-density point cloud, then performs an adaptive searching scheme for the registration procedure. As a fully interactive system, our method can handle both internal and external complex occlusions, and can quickly track back the object even when losing track. In order to further improve the realism of the projected virtual textures, our system innovatively culls occlusions away from projection, which is achieved by a facet-covering method. As a result, the Pmomo system enables the possibility of new interactive Augmented Reality applications that require high-quality dynamic projection effect.

Spatio-Temporal Learning of Basketball Offensive Strategies Poster Session 2 / Chen, Ching-Hang / Liu, Tyng-Luh / Wang, Yu-Shuen / Chu, Hung-Kuo / Tang, Nick C. / Liao, Hong-Yuan Mark Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.1123-1126

ACM Digital Library Link

Summary: Video-based group behavior analysis is drawing attention to its rich applications in sports, military, surveillance and biological observations. The recent advances in tracking techniques, based on either computer vision methodology or hardware sensors, further provide the opportunity of better solving this challenging task. Focusing specifically on the analysis of basketball offensive strategies, we introduce a systematic approach to establishing unsupervised modeling of group behaviors. In view that a possible group behavior (offensive strategy) could be of different duration and represented by dynamic player trajectories, the crux of our method is to automatically divide training data into meaningful clusters and learn their respective spatio-temporal model, which is established upon Gaussian mixture regression to account for intra-class spatio-temporal variations. The resulting strategy representation turns out to be flexible that can be used to not only establish the discriminant functions but also improve learning the models. We demonstrate the usefulness of our approach by exploring its effectiveness in analyzing a set of given basketball video clips.

Skeleton-augmented Human Action Understanding by Learning with Progressively Refined Data Detection of Events in Video / Wei, Shih-En / Tang, Nick C. / Lin, Yen-yu / Weng, Ming-Fang / Liao, Hong-Yuan Mark Proceedings of the 2014 International Workshop on Human Centered Event Understanding from Multimedia 2014-11-07 p.7-10

ACM Digital Library Link

Summary: With the aim at accurate action video retrieval, we firstly present an approach that can infer the implicit skeleton structure for a query action, an RGB video, and then propose to expand this query with the inferred skeleton for improving the performance of retrieval. It is inspired by the observation that skeleton structures can compactly and effectively represent human actions, and are helpful in bridging the semantic gap in action retrieval. The focal point is hence on action skeleton estimation in RGB videos. Specifically, an iterative training procedure is developed to select relevant training data for inferring the skeleton of an input action, since corrupt training data not only degrades performance but also complicates the learning process. Through the iterations, relevant training data are gradually revealed, while more accurate skeletons are inferred with the refined training set. The proposed approach is evaluated on ChaLearn 2013. Significant performance gains in action retrieval are achieved with the aid of the inferred skeletons.

The effects of virtual characters on audiences' movie experience / Lin, Tao / Morishima, Shigeo / Maejima, Akinobu / Tang, Ningjiu Interacting with Computers 2010 v.22 n.3 p.218-229

DOI: 10.1016/j.intcom.2009.11.010

Keywords: User experience / Virtual character / Physiological evaluation

Link to Article at sciencedirect

Summary: In this paper, we first present a new audience-participating movie form in which 3D virtual characters of audiences are constructed by computer graphics (CG) technologies and are embedded into a in a pre-rendered movie as different roles. Then, we investigate how the audiences respond to these virtual characters using physiological and subjective evaluation methods. To facilitate the investigation, we present three versions of a movie to an audience -- a Traditional version, its SDIM version with the participation of the audience's virtual character, and its SFDIM version with the co-participation of the audience and her/his friends' virtual characters. The subjective evaluation results show that the participation of virtual characters indeed causes increased subjective sense of spatial presence and engagement, and emotional reaction; moreover, SFDIM performs significantly better than SDIM, due to the co-participation of friends' virtual characters. Also, we find that the audiences experience not only significantly different galvanic skin response (GSR) changes on average -- changing trend over time and number of fluctuations -- but they also show the increased phasic GSR responses to the appearance of their own or friends' virtual 3D characters on the screen. The evaluation results demonstrate the success of the new audience-participating movie form and contribute to understanding how people respond to virtual characters in a role-playing entertainment interface.

Space-economical partial gram indices for exact substring matching DB string databases, blogs, & social search / Tang, Nan / Sidirourgos, Lefteris / Boncz, Peter Proceedings of the 2009 ACM Conference on Information and Knowledge Management 2009-11-02 p.285-294

ACM Digital Library Link

Summary: Exact substring matching queries on large data collections can be answered using q-gram indices, that store for each occurring q-byte pattern an (ordered) posting list with the positions of all occurrences. Such gram indices are known to provide fast query response time and to allow the index to be created quickly even on huge disk-based datasets. Their main drawback is relatively large storage space, that is a constant multiple (typically >2) of the original data size, even when compression is used. In this work, we study methods to conserve the scalable creation time and efficient exact substring query properties of gram indices, while reducing storage space. To this end, we first propose a partial gram index based on a reduction from the problem of omitting indexed q-grams to the set cover problem. While this method is successful in reducing the size of the index, it generates false positives at query time, reducing efficiency. We then increase the accuracy of partial grams by splitting posting lists of frequent grams in a frequency-tuned set of signatures that take the bytes surrounding the grams into account. The resulting qs-gram scheme is tested on huge collections (up to 426GB) and is shown to achieve an almost 1:1 data:index size, and query performance even faster than normal gram methods, thanks to the reduced size and access cost.

Approximate Ad-Hoc Query Engine for Simulation Data Information Search and Retrieval in Digital Libraries / Abdulla, Ghaleb / Baldwin, Chuck / Critchlow, Terence / Kamimura, Roy / Lozares, Ida / Musick, Ron / Tang, Nu Ai / Lee, Byung S. / Snapp, Robert JCDL'01: Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries 2001-06-24 p.255-256

Keywords: data integration, data retrieval, mesh data, query, scientific data management, visualization

Broken Link to ACM Digital Library

Summary: In this paper, we describe AQSim, an ongoing effort to design and implement a system to manage terabytes of scientific simulation data. The goal of this project is to reduce data storage requirements and access times while permitting ad-hoc queries using statistical and mathematical models of the data. In order to facilitate data exchange between models based on different representations, we are evaluating using the ASCI common data model that is comprised of several layers of increasing semantic complexity. To support queries over the spatial-temporal mesh structured data we are in the process of defining and implementing a grammar for MeshSQL.