Provides an abstract of the invited presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

Many enterprises in industries start using Cloud Computing for their IT infrastructure services. This adoption of Cloud Computing is a part of the enterprise transformation which is the migration from a legacy IT environment to Cloud Computing. On the other hand, one of major targets is an industry solution which provides a critical business service to their end customers. This paper proposes Industry Cloud which is the enhanced design of Cloud Computing for industry solutions. It efficiently supports industry solutions for enterprise business requirements. The paper describes Industry Cloud with a requirement analysis of industry solutions, those adopted functions, and three use case scenarios in the electronics and retail industry. The contribution of the paper is the analysis of industry wide requirements, the definition of Industry Cloud with a common function among industry solutions and the usage with use case scenarios.

Cloud Adoption decisions tend to involve multiple, conflicting criteria (attributes) with incommensurable units of measurements, which must be compared among multiple alternatives using imprecise and incomplete available information. Multi-attribute Decision Making (MADM) has been shown to provide a rational basis to aid decision making in such scenarios. We present a MADMAC framework for cloud adoption, consisting of 3 Decision Areas (DA) referred to as the Cloud Switch, Cloud Type and Vendor Choice. It requires the definition of Attributes, Alternatives and Attribute Weights, to construct a Decision Matrix and arrive at a relative ranking to identify the optimal alternative. We also present a taxonomy organized in a two level hierarchy: Server-centric clouds, Client-centric clouds and Mobile-centric clouds, which further map to detailed, specific applications or workloads. DSS presented showing algorithms derived from MADMAC can compute and optimize CA decisions separately for the three stages, where the attributes differently influence CA decisions. A modified Wide-band Delphi method is proposed for assessing the relative weights for each attribute, by workload. Relative ranks are calculated using these weights, and the Simple Additive Weighting (SAW) method is used to generate value functions for all the alternatives, and rank the alternatives by their value to finally choose the best alternative. Results from application of the method to four different types of workloads show that the method converges on reasonable cloud adoption decisions. MADMAC's key advantage is its fully quantitative and iterative convergence approach based on proven multi-attribute decision methods, which enables decision makers to comparatively assess the relative robustness of alternative cloud adoption decisions in a defensible manner. Being amenable to automation, it can respond well to even complex arrays of decision criteria inputs, unlike human decision makers. It can be implemented as a web-based DSS to readily support cloud decision making world-wide, and improved further using fuzzy TOPSIS methods, to address concerns about preferential inter-dependence of attributes, insufficient input data or judgment expertise.

Consumers are increasingly relying on Web-based social content, such as product reviews, prior to making to a purchase. Recent surveys in the retail industry confirm that social content is indeed the #1 aid in a buying decision. Currently, accessing or adding to this valuable Web-based social content repository is mostly limited to computers far removed from the site of the shopping experience itself. We present a mobile augmented reality application, which extends such social content from the computer monitor into the physical world through mobile phones, providing consumers with in situ information on products right when and where they need to make buying decisions.

In many technical support systems, live support agents help end-users resolve issues with computer software/hardware and appliances/gadgets in the real world. The dominant mode of communication in such systems is still the telephone, while instant messaging, video communication, and remote take-over have emerged as additional modalities in recent years. In contrast to these, 3D avatar based visual co-presence offers a unique combination of gestural interaction, shared reality, agent multitasking, and anonymity. Our paper argues that such 3D co-presence is viable in computer chat/remote help sessions, as well as real world support over camera equipped devices, offering an attractive alternative and enhancement to today's support modalities.

Rapid advances in mobile computing devices and sensor technologies are enabling the capture of unprecedented volumes of data by individuals involved in field operations in a variety of applications. As capture becomes ever more rich and pervasive the biggest challenge is in developing information processing and representation tools that maximize the utility of the captured multi-sensory data. The right tools hold the promise of converting captured data into actionable intelligence resulting in improved memory, enhanced situational understanding, and more efficient execution of operations. These tools need to be at least as rich and diverse as the sensors used for capture, and need to be unified within an effective system architecture. This paper presents our initial attempt at such a system and architecture that combines several emerging sensor technologies, state of the art analytic engines, and multi-dimensional navigation tools, into an end-to-end electronic chronicling solution for mobile surveillance by humans.

Current tools for contact centers provide simple mechanisms for manually logging a technical problem, and recording the route of the problem, from agent to agent, until a solution is identified and then recorded in a "problem ticket." These tools fall far short of accurately capturing the intricacies of technical problems, or providing the details of solution procedures to the right people at the right time. We present a multimedia chronicling system called blogIT that enhances the richness of capture of problem resolution activities, provides the ability to share and socially annotate experiences and solutions, and enables automatic retrieval of relevant solutions in response to a user's desktop activities.

Continuing strides in processing, storage, sensing, and networking technologies are enabling people to capture their activities and experiences as greater volumes of ever-richer media. A big emerging challenge today is the organization, retrieval, and exploitation of such multimedia data surrounding the activities of individuals or enterprises. The field of multimedia electronic chronicles deals with the unified contextual organization, presentation, and analysis of temporal streams of multimedia data captured by individuals, groups, or organizations. The value of electronic chronicles is in converting activity and experience from the past into actionable intelligence in the present. Such multimedia electronic chronicles, with their associated techniques for search and navigation, analysis and reasoning, and prediction and alerting, will have enormous impact on various spheres of life spanning enhancement of personal life, business productivity, entertainment, and government operations. This paper, which serves as a companion to a keynote talk by the first author at ICME 2005, explains the notion of electronic chronicles and outlines the research challenges in this area

Several researchers have pointed out and begun to demonstrate that it is now possible to digitize an entire lifetime of experiences into a pocket-sized storage device, and thus to create a rich and portable electronic chronicle of an individual's life and activities. This possibility has brought with it a host of significant challenges: How can the creation of these electronic chronicles be natural and effortless? How should these chronicles be organized? What kinds of navigation and search tools unleash the potential of the chronicled data allowing the user to receive and retrieve the information most relevant to their context at any time? How can such chronicles impact business organizations? How can people in organizations share chronicles and effectively combine chronicled data from different individuals in collaborative settings? Our research is beginning to address these issues with a focus on the business organizational setting. In this paper we introduce PICASSO, our work on pervasive chronicle creation and exploitation in enterprise applications. PICASSO enables the capture of rich user context (including PC/PDA interactions, audio, video, images, and location) and provides tools that enable users to search, navigate, share, and merge personal events, both from desktop computers and from mobile devices

Projected interfaces are gaining popularity as a means of access to information anywhere in a space without the need for special devices or monitors. In this context, it is of particular interest to automatically steer the projected interface to appropriate surfaces based on the location of the user in the space. This requires the users be tracked precisely, and in real time, as they move about in the environment, in spite of continuously varying and moving projected imagery in the background. This is challenging as the projection regions also appear as foreground. This paper presents a geometric image masking technique that separately segments out regions corresponding to the user and projected imagery in multiple camera views, and enables precise three-dimensional tracking of the user.

We demonstrate a system that combines steerable projection and computer vision technologies to create "touch-screen" style interactive displays on any flat surface in a space. A high-end version of the system -- the Everywhere Display (ED) -- combines an LCD projector with motorized focus and zoom, a computer controlled pan-tilt mirror, and a pan-tilt zoom camera to enable steering of interactive projections around space. A low-end version (ED-lite) enables creation of interactive displays using a portable projector and camera attached to a laptop computer. Unlike traditional augmented reality systems, the ED systems enable delivery of interactive multimedia content on ordinary objects without requiring users to wear head mounted displays or carry special input devices.

This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.

Estimation of human head orientation is important for a number of applications such as human-computer interaction, teleconferencing, virtual reality, and 3D audio rendering. We present a system for estimating human head orientation based on visual information. Two neural networks are trained to approximate the functions that map an image of a head to the orientation of the head. We obtain ground-truth data for training and testing from an electromagnetic tracking device worn by subjects. Our experimental results demonstrate orientation accuracy within 10/spl deg/ with the subject free to move about at distances of three to ten feet from the camera. The system is designed to be robust to illumination changes and it runs in real time.

Traditionally, a user has positioned himself/herself to be in front of a display in order to access information from it. In this information age, life at work and even at home is often confined to be in front of a display device that is the source of information or entertainment. The paper introduces another paradigm where the display follows the user rather than the user being tied to the display. We demonstrate how steerable projection and people tracking can be combined to achieve a display that automatically follows the user.

A serious problem in both audio and video conferencing facilities available today is the difficulty in determining who is speaking among a large number of participants. There is a strong need for developing meeting room infrastructure and teleconference facilities that improve the sense of presence and participation experienced in remote meetings. We present a distributed multimodal tracking system that uses multiple cameras and microphones to automatically select the current speaker among multiple meeting participants. The system actively obtains and transmits video showing a good view of the selected speaker. The tracking system is integrated into a web-based video conferencing application that connects seven meeting rooms around the globe. An important part of designing such a system is to determine sensor placement and configuration through systematic experiments in the actual rooms where the system is deployed.

We introduce a new paradigm for real-time conversion of a real world event into a rich multimedia database by processing data from multiple sensors observing the event. A real-time analysis of the sensor data, tightly coupled with domain knowledge, results in instant indexing of multimedia data at capture time. This yields semantic information to answer complex queries about the content and the ability to extract portions of data that correspond to complex actions performed in the real world. The power of such an instantly indexed multimedia database system, in content-based retrieval of multimedia data or in semantic analysis and visualization of the data, far exceeds that of systems which index multimedia data only after it is produced. We present LucentVision, an instantly indexed multimedia database system developed for the sport of tennis. This system analyzes video from multiple cameras in real time and captures the activity of the players and the ball in the form of motion trajectories. The system stores these trajectories in a database along with video, 3D models of the environment, scores, and other domain-specific information. LucentVision has been used to enhance live television and Internet broadcasts with game analyses and virtual replays in more than 250 international tennis matches.

As multiple video cameras and other sensors generate very large quantities of multimedia data in media productions and surveillance applications, a key challenge is to identify the relevant portions of the data and to rapidly retrieve the corresponding sensor data. Spatio-temporal activity maps serve as an efficient and intuitive graphical user interface for multimedia retrieval, particularly when the media streams are derived from multiple sensors observing a physical environment. We formulate the media retrieval problem in this context, and develop an architecture for interactive media retrieval by combining spatio-temporal "activity maps" with domain specific event information. Activity maps are computed from trajectories of motion of objects in the environment, which in turn are derived automatically by analysis of sensor data. We present an activity map based video retrieval system for the sport of tennis and demonstrate that the activity map based scheme significantly helps the user in a) discovering the relevant portions of the data, and b) non-linearly retrieving the corresponding media streams.

Remote experience of sporting events has thus far been limited mostly to watching video and the scores and statistics associated with the sport. However, a fast-developing trend is the use of visualization techniques to give new insights into performance, style, and strategy of the players. Automated techniques can extract accurate information from video about player performance that not even the most skilled observer is able to discern. When presented as static images or as a three-dimensional virtual replay, this information makes viewing a game an entirely new and exciting experience. This paper presents one such sports visualization system called LucentVision, which has been developed for the sport of tennis. LucentVision uses real-time video analysis to obtain motion trajectories of players and the ball, and offers a rich set of visualization options based on this trajectory data. The system has been used extensively in the broadcast of international tennis tournaments, both on television and the Internet.

Presents a real-time computer vision system that tracks the motion of a tennis ball in 3D using multiple cameras. Ball tracking enables virtual replays, new game statistics, and other visualizations which result in very new ways of experiencing and analyzing tennis matches. The system has been used in international television broadcasts and webcasts of more than 15 matches. Six cameras around a stadium, divided into four pairs, are currently used to track the ball on serves which sometimes exceed speeds of 225 kmph. A multi-threaded approach is taken to tracking where each thread tracks the ball in a pair of cameras based on motion, intensity and shape, performs stereo matching to obtain the 3D trajectory, detects when a ball goes out of view of its camera pair, and initializes and triggers a subsequent thread. This efficient approach is scalable to many more cameras tracking multiple objects. The ready acceptance of the system indicates the growing potential for multi-camera based real-time tracking in broadcast applications.

The goal in user interfaces is natural interactivity unencumbered by sensor and display technology. In this paper, we propose that a multi-modal approach using inverse modeling techniques from computer vision, speech recognition, and acoustics can result in such interfaces. In particular, we demonstrate a system for audio-visual tracking, showing that such a system is more robust, more accurate, more compact, and yields more information than using a single modality for tracking. We also demonstrate how such a system can be used to find the talker among a group of individuals, and render 3D scenes to the user.

This paper develops real time tracking technology for sports broadcast applications. The specific sport chosen here is the game of tennis. The outputs of the tennis tracking system are spatio-temporal trajectories of motion of the players and the ball which can in turn provide a number of statistics about the game. For instance, the distance travelled by a player, the speed and the acceleration at any instant, as well as court coverage patterns can be obtained from the trajectories. The statistics so obtained can be visualized in compelling ways to enhance the appreciation of the athleticism and strategy involved in the sport. We present techniques for tracking the players and the ball in video obtained from stationary cameras. The problem is challenging as the tracking needs to be performed outdoors, players are fast-moving non-rigid objects, and the ball is a small object that can move at speeds in the range of 150 miles an hour. Player trajectories are obtained by dynamically clustering tracks of local features. Ball segmentation and tracking is based on shape and color features of the ball. Real time tracking results are presented on video recorded live by the authors in an international tennis tournament.

Virtual environment research has focused on interactive image generation and has largely ignored acoustic modeling for spatialization of sound.Yet, realistic auditory cues can complement and enhance visual cues to aid navigation, comprehension, and sense of presence in virtual environments.A primary challenge in acoustic modeling is computation of reverberation paths from sound sources fast enough for real-time auralization.We have developed a system that uses precomputed spatial subdivision and "beam tree" data structures to enable real-time acoustic modeling and auralization in interactive virtual environments.The spatial subdivision is a partition of 3D space into convex polyhedral regions (cells) represented as a cell adjacency graph.A beam tracing algorithm recursively traces pyramidal beams through the spatial subdivision to construct a beam tree data structure representing the regions of space reachable by each potential sequence of transmission and specular reflection events at cell boundaries.From these precomputed data structures, we can generate high-order specular reflection and transmission paths at interactive rates to spatialize fixed sound sources in real-time as the user moves through a virtual environment.Unlike previous acoustic modeling work, our beam tracing method: 1) supports evaluation of reverberation paths at interactive rates, 2) scales to compute highorder reflections and large environments, and 3) extends naturally to compute paths of diffraction and diffuse reflection efficiently.We are using this system to develop interactive applications in which a user experiences a virtual environment immersively via simultaneous auralization and visualization.

A tracking system outputs a separate motion trajectory for each moving object in a scene. The paper presents a problem of performance evaluation and performance metrics for real time systems that track people, or moving objects, in video sequences, and it proposes performance measurement methodology for such systems. Two approaches to measuring performance are presented. The first approach compares the computed motion trajectories to the reference trajectories. It enables a complete evaluation of tracking results, but reference trajectories it requires are difficult to get. The second, more practical approach identifies in the computed trajectories specific discrete events, such as line crossings, and compares sequences of these events to sequences of reference events, which are much easier to obtain than reference trajectories. These events can usually be chosen such that they reflect the application goal of a tracking system, e.g. counting people in an area. Precision of evaluation increases with density of events. Short event sequences measure the sensitivity and selectivity of a tracking method, i.e. how well it satisfies the "one person one trajectory" objective. Long sequences measure continuity of trajectories: how long a method can keep track of one person. The paper shows performance measurement results for a real time people tracking system developed by the authors.

Autonomous robot navigation requires the solution of several problems like obstacle avoidance and road following. Traditionally, the problem of obstacle avoidance has been solved by using range information acquired through some form of range measurements like laser range cameras or vision based range estimation. This paper presents a novel approach for obstacle avoidance which does not require true range measurement. Obstacle information is acquired using a single color camera. Detected obstacles are projected into 3D grid derived from the camera geometry and a locally flat earth model for the terrain. A weighted polar histogram technique is used to generate the vehicle steering angle based on the current grid information.>

Scanning probe microscopy (SXM), which includes techniques such as scanning tunneling microscopy (STM) and scanning force microscopy (SFM), is becoming popular for 3D metrology in the semiconductor industry and for high resolution 3D imaging of surfaces in Materials Science and Biology. The authors present imaging models for SXM that take into account the effect of probe geometry on topographic images produced by SXM in 'contact' and 'non-contact' modes. The authors formulate methods for restoring an SXM image to obtain the original surface. Criteria for determining certainty of restoration are developed. It is shown that the methods developed can be expressed in terms of gray scale morphological operators. The efficacy of the approach is demonstrated by applying it to synthetic and real data.>