14456979
Supporting Quality of Service in IP multicast networks
Developers and providers of data, voice and video applications have to satisfy numerous Quality of Service (QoS) requirements and cope with different architectures emerging to provide these QoS in the Internet. IntServ, DiffServ or MPLS are technologies currently used in different administrative domains in the Internet to provide QoS. With multicast applications emerging, such as video conferencing, distance learning and tele surgery, it has become almost impossible to provide end-to-end QoS over multiple administrative business domains without interworking these technologies. This paper analyses general issues on supporting QoS for multicast applications and reviews different ways to implement IP multicasting in IntServ, DiffServ or MPLS based networks. Subsequently, network configurations based on these different architectures and their interworking to provide end-to-end QoS are also discussed.
2557933
Packet loss probability for DiffServ over IP and MPLS reliable homogeneous multicast networks
Multicasting has become increasingly important with the emergence of Internet-based applications such as IP telephony, audio/video conferencing, distributed databases and software upgrading. IP multicasting is an efficient way to distribute information from a single source to multiple destinations at different locations. In practice IP is considered as a layer 3 protocol. Multiprotocol Label Switching (MPLS) replaces the IP forwarding by a simple label lookup. MPLS combines the flexibility of layer 3 routing and layer 2 switching.In order to provide QoS in group communications for real time applications such as video conferencing, reliable multicasting is used. Miscellaneous efforts have been undertaken to provide reliability on top of IP multicast. Two error control strategies have been popular in practice. These are the FEC (Forward Error Correction) strategy, which uses error correction alone, and the ARQ (Automatic Repeat Request) strategy, which uses error detection, combined with retransmission of data.In this paper, we present a new fair share policy (FSP) that utilizes Differentiated Services to solve the problems of QoS and congestion control when reliable ARQ multicast is used. The results should provide insight into the comparisons of the residual packet loss probability between IP multicast in MPLS networks using FSP and plain IP multicasting using the same policy when DiffServ are adopted and when reliable ARQ multicast is considered.
7563707
Learning to track 3D human motion from silhouettes
We describe a sparse Bayesian regression method for recovering 3D human body motion directly from silhouettes extracted from monocular video sequences. No detailed body shape model is needed, and realism is ensured by training on real human motion capture data. The tracker estimates 3D body pose by using Relevance Vector Machine regression to combine a learned autoregressive dynamical model with robust shape descriptors extracted automatically from image silhouettes. We studied several different combination methods, the most effective being to learn a nonlinear observation-update correction based on joint regression with respect to the predicted state and the observations. We demonstrate the method on a 54-parameter full body pose model, both quantitatively using motion capture based test sequences, and qualitatively on a test video sequence.
958128
The Panum Proxy Algorithm for Dense Stereo Matching over a Volume of Interest
Stereo matching algorithms conventionally match over a range of disparities sufficient to encompass all visible 3D scene points. Human vision however does not do this. It works over a narrow band of disparities - Panum’s fusional band - whose typical range may be as little as 1/20 of the full range of disparities for visible points. Points inside the band are fused visually and the remainder of points are seen as "diplopic" - that is with double vision. The Panum band restriction is important also in machine vision, both with active (pan/tilt) cameras, and with high resolution cameras and digital pan/tilt.
39910433
Augmented neural networks for task scheduling
Abstract We propose a new approach, called Augmented Neural Networks (AugNN) for solving the task-scheduling problem. This approach is a hybrid of the heuristic and the neural networks approaches. While retaining all the advantages of the heuristic approach, AugNN incorporates learning, to find improved solutions iteratively. This new framework maps the problem structure to a neural network and utilizes domain specific knowledge for finding solutions. The problem we address is that of minimizing the makespan in scheduling n tasks on m machines where the tasks follow a precedence relation and task pre-emption is not allowed. Solutions obtained from AugNN using various learning rules are compared with six different commonly used heuristics. AugNN approach provides significant improvements over heuristic results. In just a few iterations, the gap between the lower bound and the obtained solution is reduced by as much as 58% for some heuristics, without any increase in computational complexity. While the heuristics found solutions in the range of 5.8–26.9% of the lower bound, on average, AugNN found solutions in the range of 3.3–11.1%, a significant improvement.
12571939
Inflight Rerouting for an Unmanned Aerial Vehicle
The availability of new information can often necessitate rerouting of a vehicle. In (1), the set of nonoverlapping useful vicinities of the reconnaissance sites, S, and the 3D space over them was represented using a discrete graph-model. Here, we consider two cases in which an unmanned reconnaissance aerial vehicle must be rerouted. The first scenario involves not visiting useful vicinities, S t ⊂ S, due to newly reported unacceptable threat- levels in their neighborhood. In the second scenario, visitations to useful vicinities, S r ⊂ S, is made redundant due to decreased importance of the sites to which they offer a vantage observation-point. Our earlier graph-model (1) is general enough to provide a basic framework for consideration of rerouting in both cases. We give an algorithm to obtain a graph-model, from the earlier one, that allows efficient gene encoding and efficient application of GA operators to obtain new alternative rerouting plans.
9880007
A Local Basis Representation for Estimating Human Pose from Cluttered Images
Recovering the pose of a person from single images is a challenging problem. This paper discusses a bottom-up approach that uses local image features to estimate human upper body pose from single images in cluttered backgrounds. The method takes the image window with a dense grid of local gradient orientation histograms, followed by non negative matrix factorization to learn a set of bases that correspond to local features on the human body, enabling selective encoding of human-like features in the presence of background clutter. Pose is then recovered by direct regression. This approach allows us to key on gradient patterns such as shoulder contours and bent elbows that are characteristic of humans and carry important pose information, unlike current regressive methods that either use weak limb detectors or require prior segmentation to work. The system is trained on a database of images with labelled poses. We show that it estimates pose with similar performance levels to current example-based methods, but unlike them it works in the presence of natural backgrounds, without any prior segmentation.