Segmentation of Positron Emission Tomography Images Using Multi-atlas Anatomical Magnetic Resonance Imaging (MRI)

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Videoconferencing over OpenFlow Networks: An Optimization Framework for QoS Routing

International audienceThe recent emergence of OpenFlow, in the field of networks' control, marks a major evolution in networking. Indeed, Openflow comes up with a centralized control plane (real or virtual) and a remote control of the data plan, which enable network's evolution with unprecedented rapidity. This evolution, come, however, with new challenges and open issues. The data path portion still resides on networking devices, while high-level routing decisions are moved to a separate remote server, which may impact the QoS of the supported services. Particularly, there is a need to guarantee at least the QoS that was formally provided using older and classical distributed protocols. This paper focuses on an improvement of the traditional OpenFlow controllers by providing QoS Routing support for Videoconfer-encing over OpenFlow networks. The simulation results under Mininet clearly demonstrate the effectiveness of the proposed approach

Probe-SDN: a smart monitoring framework for SDN-based networks

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Temporal and spatial coherence verification in SMIL documents with hoare logic and disjunctive constraints: A hybrid formal method

© 2016 - Society for Design and Process Science. All rights reserved. The challenging problem of formal verification of SMIL (Synchronized Multimedia Integration Language Specification) documents is considered in this paper, where we propose a hybrid formal method that automatically detects and corrects the temporal and spatial conflicts. The proposed solution is based on a related work that uses Hoare logic for temporal conflict detection in SMIL documents. The use of Hoare logic is borrowed by that solution but with many improvements and extensions. New rules are added to enable modeling of more SMIL elements. We also deal with spatial conflicts and propose a spatio-temporal inconsistencies verification algorithm, called Spatio-temporal Inconsistencies Verification Algorithm (SIVA), that checks the spatial incoherence of SMIL documents. The disjunctive constraints of Marriott are used to correct the spatial inconsistencies. Furthermore, we propose a new tool that helps the author to validate the temporal and spatial constraints in SMIL documents. If any temporal or spatial conflicts are detected, the system returns a help message to report the error and help the author to correct the conflict. Finally, our contribution has been compared with two recent related works, and the results show that the proposed solution allow to check more attributes

Mobile traffic forecasting using a combined FFT/LSTM strategy in SDN networks

International audienceOver the last few years, networks' infrastructures are experiencing a profound change initiated by Software Defined Networking (SDN) and Network Function Virtualization (NFV). In such networks, avoiding the risk of service degradation increasingly involves predicting the evolution of metrics impacting the Quality of Service (QoS), in order to implement appropriate preventive actions. Recurrent neural networks, in particular Long Short Term Memory (LSTM) networks, already demonstrated their efficiency in predicting time series, in particular in networking, thanks to their ability to memorize long sequences of data. In this paper, we propose an improvement that increases their accuracy by combining them with filters, especially the Fast Fourier Transform (FFT), in order to better extract the characteristics of the time series to be predicted. The proposed approach allows improving prediction performance significantly, while presenting an extremely low computational complexity at run-time compared to classical techniques such as Auto-Regressive Integrated Moving Average (ARIMA), which requires costly online operations

Anatomical multiatlas segmentation using local texture statistical properties for matching descriptor with machine learning

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OVIS: ontology video surveillance indexing and retrieval system

International audienceNowadays, the diversity and large deployment of video recorders results in a large volume of video data, whose effective use requires a video indexing process. However, this process generates a major problem consisting in the semantic gap between the extracted low-level features and the ground-truth. The ontology paradigm provides a promising solution to overcome this problem. However, no naming syntax convention has been followed in the concept creation step, which constitutes another problem. In this paper, we have considered these two issues and have developed a full video surveillance ontology following a formal naming syntax convention and semantics that addresses queries of both academic research and industrial applications. In addition, we propose an Ontology Video-surveillance Indexing and retrieval System (OVIS) using a set of Semantic Web Rule Language (SWRL) rules that bridges the semantic gap problem. Currently, the existing indexing systems are essentially based on low-level features and the ontology paradigm is used only to support this process with representing surveillance domain. In this paper, we developed the OVIS system based on the SWRL rules and the experiments prove that our approach leads to promising results on the top video evaluation benchmarks and also shows new directions for future developments

Events detection using a video-surveillance Ontology and a rule-based approach

International audienceIn this paper, we propose the use of a Video-surveillance Ontology and a rule-based approach to detect an event. The scene is described using the concepts presented in the ontology. Then, the blobs are extracted from the video stream and are represented using the bounding boxes that enclose them. Finally, a set of rules have been proposed and have been applied to videos selected from PETS 2012 challenge that contain multiple objects events (e.g. Group walking, Group splitting, etc.)

An ontology based approach for inferring multiple object events in surveillance domain

International audienceThe ontology is an efficient tool that can bridge the semantic gap between the extracted information from the visual data and its interpretation in a given context. The ontology has been used in video surveillance applications to improve the accuracy of the indexing and retrieval system. However, these systems handle only one or two objects without considering events that involve multiple objects. In this paper, we propose to use OVIS (Ontology based Video surveillance Indexing and retrieval System) a system for indexing and retrieving videos in video surveillance application. We have applied OVIS to videos that contain multiple objects events (e.g. Group walking, Group splitting, Group formation, etc.)