Selectively meshed surface representation

Surface rendering is an important technique for volume visualization. Any surface rendering algorithm has two phases—surface generation and rendering. We present a new surface rendering algorithm, which focuses on constructing the surface in a manner that speeds up the rendering phase. The motivation behind this is to reduce the response time for surface manipulations such as interactive rotations. We utilize a MC-like (Marching Cubes) approach to calculate the intersection points and their normals for each cube. But we dynamically link the intersection points to form triangles within the cube according to the locations of the last and the next visited neighboring cubes so that a good meshed surface can be generated. The difficulty with such an approach is that thousands of special cases need to be considered. But, we have found that the occurrence of five specific configurations out of the 14 basic MC cube configurations account for over 95% of all the cubes intersected by the iso-surface in most data sets. We process cubes belonging to these five configurations in a mesh mode, and the rest are processed in a non-mesh mode. As a result, the number of special cases are reduced substantially. Then a very careful analysis of the five configurations for mesh processing leads to just 136 cases, which makes the algorithm very simple. Test results show that the rendering time is almost halved compared to the time required for the rendering of a non-meshed surface generated by MC.     

Pivot vector space approach for audio-video mixing

An audio-mixing artist usually adds the musical accompaniment to video. Employing such artists is expensive and not feasible for a home video presentation. Our automatic audio-video mixing technique is suited for home videos. It uses a pivot vector space mapping method that matches video shots with music segments based on aesthetic cinematographic heuristics.     

Investigating organizational learning in eGovernment projects: A multi-theoretic approach

Organizational learning is essential during eGovernment implementation to leverage the benefits of such efforts. However, there is a lack of holistic understanding of organizational learning in IT-related change in public organizations. Motivated by such concerns, we construct a structurational framework that inter-relates elements pertinent to IT-related change from four theoretical perspectives: organizational politics, organizational culture, institutional theory, and organizational learning itself. Inter-relationships between elements of these perspectives are explicated through the notions of structures, modalities, and actions from structuration theory. Guided by the framework, we conducted a case study to examine the organizational learning occurring during a successful eGovernment project. The analysis indicates the inextricable interactions between elements from the different perspectives during the project implementation. The proposed framework offers both theoretical and practical benefits for understanding and facilitating organizational learning in eGovernment projects.     

Watermarking of Electronic Text Documents

With the tremendous development of the Internet, it has become desirable to distribute text documents electronically. However, commercial publishers may be reluctant to offer valuable digital documents online for the fear that they will be re-transmitted or copied illegally. To address this problem, we propose a robust watermarking technique, whereby electronic text documents are fingerprinted with one or more semantics-preserving modifications to the document text. The text modifications may be selected so that multiple copies of the same master document will all have the same meaning. By examining text modifications in an unauthorized copy, one can identify the authorized source and the recepient. In this paper, we present a new method that is accurate, robust against attacks (e.g., the cyber pirate may post only a section or a paragraph of a registered text online), scalable (e.g., a few pages of text to hundreds of pages) and secure (e.g., remove or modify embedded watermark with or without knowledge of watermarking method). This approach could therefore facilitate e-commerce of newspapers, journals, magazines, and in general any electronic text document possessing commercial value.     

Automatic partitioning of full-motion video

Partitioning a video source into meaningful segments is an important step for video indexing. We present a comprehensive study of a partitioning system that detects segment boundaries. The system is based on a set of difference metrics and it measures the content changes between video frames. A twin-comparison approach has been developed to solve the problem of detecting transitions implemented by special effects. To eliminate the false interpretation of camera movements as transitions, a motion analysis algorithm is applied to determine whether an actual transition has occurred. A technique for determining the threshold for a difference metric and a multi-pass approach to improve the computation speed and accuracy have also been developed.     

W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video

Huge amounts of video are being recorded every day by surveillance systems. Since video is capable of recording and preserving an enormous amount of information which can be used in many applications, it is worth examining the degree of privacy loss that might occur due to public access to the recorded video. A fundamental requirement of privacy solutions is an understanding and analysis of the inference channels than can lead to a breach of privacy. Though inference channels and privacy risks are well studied in traditional data sharing applications (e.g., hospitals sharing patient records for data analysis), privacy assessments of video data have been limited to the direct identifiers such as people’s faces in the video. Other important inference channels such as location (Where), time (When), and activities (What) are generally overlooked. In this paper we propose a privacy loss model that highlights and incorporates identity leakage through multiple inference channels that exist in a video due to what, when, and where information. We model the identity leakage and the sensitive information separately and combine them to calculate the privacy loss. The proposed identity leakage model is able to consolidate the identity leakage through multiple events and multiple cameras. The experimental results are provided to demonstrate the proposed privacy analysis framework.     

Senior Citizens' Acceptance of Information Systems: A Study in the Context of e-Government Services

The turn of the century has seen a rapidly graying population concurrently with increasingly ubiquitous information technology (IT). The elderly population being less familiar with IT implies that issues pertaining to their acceptance of information systems (IS) deserve special attention. However, the IS and gerontology literatures seldom explore IS acceptance among the aged. Attempting to address this gap, a research model is developed by integrating relevant antecedents from previous literature and empirically tested in the context of an e-Government service tailored for senior citizens. Our findings reveal that consistent with previous technology acceptance studies, senior citizens' use intention is driven by their perceived usefulness and perceived ease of use of the service. Additionally, their Internet safety perception impacts the acceptance of this online financial service. Furthermore, resource savings and self-actualization determine senior citizens' usefulness perception, whereas computer anxiety and computing support are antecedents of perceived ease of use. Of these antecedents, self actualization and computer anxiety appear particularly relevant for the elderly as compared to younger working professionals in most previous literature for whom these antecedents have not been important. The study contributes by providing an integrative model of IS acceptance among the aged, which may facilitate theory development and practice     

Video modeling using strata-based annotation

Our project on digital video modeling aims to achieve efficient browsing and retrieval. Video is not merely a huge collection of still images, it is a complex temporal medium capturing high-level semantic ideas. For almost a decade researchers have worked on developing efficient techniques to model, index, and retrieve digital video information. To support intuitive video access, the video data must be properly modeled and indexed based on its characteristics and contents. One such annotation-based model is the stratification model, which focuses on segmenting the video's contextual information into multiple layers or strata. Each stratum describes the temporal occurrences of a simple concept such as the appearance of an anchor person in a news video. It thus focuses on segmenting the contextual information into chunks rather than dividing physically contiguous frames into shots, as is traditionally done