677559
TEEVE: the next generation architecture for tele-immersive environments
Tele-immersive 3D multi-camera room environments are starting to emerge and with them new challenging research questions. One important question is how to organize the large amount of visual data, being captured, processed, transmitted and displayed, and their corresponding resources, over current COTS computing and networking infrastructures so that "everybody" would be able to install and use tele-immersive environments for conferencing and other activities. In this paper, we propose a novel cross-layer control and streaming framework over general purpose delivery infrastructure, called TEEVE (tele-immersive environments for everybody). TEEVE aims for effective and adaptive coordination, synchronization, and soft QoS-enabled delivery of tele-immersive visual streams to remote room(s). The TEEVE experiments between two tele-immersive rooms residing in different institutions more than 2000 miles apart show that we can sustain communication of up to 12 3D video streams with 4/spl sim/5 3D frames per second for each stream, yielding 4/spl sim/5 tele-immersive video rate.
11758
A multi-stream adaptation framework for bandwidth management in 3D tele-immersion
Tele-immersive environments will improve the state of collaboration among distributed participants. However, along with the promise a new set of challenges have emerged including the real-time acquisition, streaming and rendering of 3D scenes to convey a realistic sense of immersive spaces. Unlike 2D video conferencing, a 3D tele-immersive environment employs multiple 3D cameras to cover a much wider field of view, thus generating a very large volume of data that need to be carefully coordinated, organized, and synchronized for Internet transmission, rendering and display. This is a challenging task and a dynamic bandwidth management must be in place. To achieve this goal, we propose a multi-stream adaptation framework for bandwidth management in 3D tele-immersion. The adaptation framework relies on the hierarchy of mechanisms and services that exploits the semantic link of multiple 3D video streams in the tele-immersive environment. We implement a prototype of the framework that integrates semantic stream selection, content adaptation, and 3D data compression services with user preference. The experimental results have demonstrated that the framework shows a good quality of the resulting composite 3D rendered video in case of sufficient bandwidth, while it adapts individual 3D video streams in a coordinated and user-friendly fashion, and yields graceful quality degradation in case of low bandwidth availability.
11512625
Hourglass multimedia content and service composition framework for smart room environments
Pervasive environments with their ubiquitous multimedia devices such as video-enabled cell-phones, laptops, PC tablets, digital cameras, HDTV plasma displays are becoming integral part of our workplaces and homes. Furthermore, these digital multimedia devices make it easier and easier to create and view volumes of content that can be stored throughout the Internet. The big challenge is to organize the distributed content, get the distributed multimedia content composed, produced and distributed to users in customized fashion according to users' preference, and their resource availabilities in pervasive environments. Basically, the challenge is to create Internet Interactive Television where the user/interest group, residing in a pervasive environment, corresponds to the TV program director who decides and/or influences what content(s) will be viewed, how it will be composed, presented and delivered. In this paper, we present a vision how this challenge can be solved. We propose a novel hourglass-based system that delivers composite multimedia content to users in pervasive environments, which considers both users' interest in the content and the presentation capabilities of various display devices. This hourglass-based system relies on our content and service composition framework that allows for customized production, distribution and presentation of multimedia content for pervasive environments. We present the hourglass design, possible solutions for some content composition scenarios and validate the solutions via selected experiments.
34993677
Automatic text location in images and video frames
Abstract Textual data is very important in a number of applications such as image database indexing and document understanding. The goal of automatic text location without character recognition capabilities is to extract image regions that contain only text. These regions can then be either fed to an optical character recognition module or highlighted for a user. Text location is a very difficult problem because the characters in text can vary in font, size, spacing, alignment, orientation, color and texture. Further, characters are often embedded in a complex background in the image. We propose a new text location algorithm that is suitable in a number of applications, including conversion of newspaper advertisements from paper documents to their electronic versions, World Wide Web search, color image indexing and video indexing. In many of these applications, it is not necessary to extract all the text, so we emphasize on extracting important text with large size and high contrast. Our algorithm is very fast and has been shown to be successful in extracting important text in a large number of test images.
16993770
Service matchmaking with rough sets
With the wide adoption of open grid services architecture (OGSA) and Web services resource framework (WSRF), the grid is emerging as a service-oriented computing infrastructure for engineers and scientists to solve data and computationally intensive problems. It is envisioned that computing resources in a future grid environment will be exposed as services. Service discovery becomes an issue of vital importance for a wider uptake of the grid. This paper presents RSSM, a rough sets based service matchmaking algorithm for service discovery with an aim to tolerate uncertainty in identifying service properties. The evaluation results show that the RSSM algorithm is more effective in service discovery compared with other mechanisms such as UDDI and OWLS.
2324509
Sparse Boosting
We propose Sparse Boosting (the SparseL2Boost algorithm), a variant on boosting with the squared error loss. SparseL2Boost yields sparser solutions than the previously proposed L2Boosting by minimizing some penalized L2-loss functions, the FPE model selection criteria, through small-step gradient descent. Although boosting may give already relatively sparse solutions, for example corresponding to the soft-thresholding estimator in orthogonal linear models, there is sometimes a desire for more sparseness to increase prediction accuracy and ability for better variable selection: such goals can be achieved with SparseL2Boost. 
 
We prove an equivalence of SparseL2Boost to Breiman's nonnegative garrote estimator for orthogonal linear models and demonstrate the generic nature of SparseL2Boost for nonparametric interaction modeling. For an automatic selection of the tuning parameter in SparseL2Boost we propose to employ the gMDL model selection criterion which can also be used for early stopping of L2Boosting. Consequently, we can select between SparseL2Boost and L2Boosting by comparing their gMDL scores.
205431881
An integrated approach to high-level information fusion
In today's fast paced military operational environment, vast amounts of information must be sorted out and fused not only to allow commanders to make situation assessments, but also to support the generation of hypotheses about enemy force disposition and enemy intent. Current information fusion technology has the following two limitations. First, current approaches do not consider the battlefield context as a first class entity. In contrast, we consider situational context in terms of terrain analysis and inference. Second, there are no integrated and implemented models of the high-level fusion process. This paper describes the HiLIFE (High-Level Information Fusion Environment) computational framework for seamless integration of high levels of fusion (levels 2, 3 and 4). The crucial components of HiLIFE that we present in this paper are: (1) multi-sensor fusion algorithms and their performance results that operate in heterogeneous sensor networks to determine not only single targets but also force aggregates, (2) computational approaches for terrain-based analysis and inference that automatically combine low-level terrain features (such as forested areas, rivers, etc.) and additional information, such as weather, and transforms them into high-level militarily relevant abstractions, such as NO-GO, SLOW-GO areas, avenues of approach, and engagement areas, (3) a model for inferring adversary intent by mapping sensor readings of opponent forces to possible opponent goals and actions, and (4) sensor management for positioning intelligence collection assets for further data acquisition. The HiLIFE framework closes the loop on information fusion by specifying how the different components can computationally work together in a coherent system. Furthermore, the framework is inspired by a military process, the Intelligence Preparation of the Battlefield, that grounds the framework in practice. HiLIFE is integrated with a distributed military simulation system, OTBSAF, and the RETSINA multi-agent infrastructure to provide agile and sophisticated reasoning. In addition, the paper presents validation results of the automated terrain analysis that were obtained through experiments using military intelligence Subject Matter Experts (SMEs).
206807096
Multiple copy image denoising via wavelet thresholding
This work addresses the recovery of an image from noisy observations when multiple noisy copies of the image are available. The standard method is to compute the average of these copies. Since the wavelet thresholding technique has been shown to effectively denoise a single noisy copy, it is natural to consider combining these two operations of averaging and thresholding. The first important task is to find the optimal ordering. The second issue is the threshold selection for each method. By modeling the signal wavelet coefficients as Laplacian distributed and the noise as Gaussian, our investigation finds the optimal ordering to depend on the number of available copies and on the signal-to-noise ratio. We propose thresholds that are nearly optimal under the assumed model for each ordering. With the optimal and near-optimal thresholds, the two methods yield very similar performance, and both show considerable improvement over merely averaging.