Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles

In literature, several approaches are trying to make the UAVs fly autonomously i.e., by extracting perspective cues such as straight lines. However, it is only available in well-defined human made environments, in addition to many other cues which require enough texture information. Our main target is to detect and avoid frontal obstacles from a monocular camera using a quad rotor Ar.Drone 2 by exploiting optical flow as a motion parallax, the drone is permitted to fly at a speed of 1 m/s and an altitude ranging from 1 to 4 meters above the ground level. In general, detecting and avoiding frontal obstacle is a quite challenging problem because optical flow has some limitation which should be taken into account i.e. lighting conditions and aperture problem

Multi-modal image fusion for small animal studies in in-line PET /3T MRI

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).National audienceIn the framework of small animal multi-modal imaging, the current progression of the IMAPPI project is illustrated by the design of an in-line PET/MRI prototype, coupled to a dedicated multi-resolution registration method allowing the robust fusion of data coming from both modalities. The first results show a good alignment of the data from tumor imaging at the level of the abdomen

NoC évolutif à l'exécution pour les accélérateurs basés sur FPGA virtualisés en tant que services cloud

Ces dernières années, les fournisseurs de cloud et les centres de données ont intégrés les FPGA dans leur environnement à des fins d'accélération. Cela est dû au fait que les accélérateurs à base de FPGA sont connus pour leur faible puissance et leurs bonnes performances par watt. En outre, l'introduction de la capacité de reconfiguration partielle dynamique (DPR) de certains FPGA incite les chercheurs de l'industrie et des universitaires à proposer des services de cloud FPGA virtualisés baser sur DPR. Dans la plupart des travaux existants, l'interconnexion entre les vFPGA repose soit sur les réseaux BUS ou OpenFlow. Cependant le bus et OpenFlow ne sont pas des solutions optimales pour la virtualisation.Dans cette thèse, nous avons proposé un NoC évolutif à l'exécution pour les accélérateurs basés sur FPGA virtualisés dans un cloud computing. Les composants NoC s'adapteront dynamiquement aux nombres d'accélérateurs virtualisés actifs en ajoutant et en supprimant des sous-noC. Pour minimiser la complexité de la conception de l'architecture NoC à un niveau inférieur (implémentation HDL), nous avons proposé un langage de modélisation unifié de haut niveau (UML) basé sur une ingénierie dirigée par les modèles. Une approche basée sur UML / MARTE et IP-XACT est utilisée pour définir les composants de la topologie NoC de haut niveau et générer les fichiers HDL requis. Les résultats des expériences montrent que le NoC évolutif à l'exécution peut réduire la consommation d'énergie de 17%. La caractérisation NoC sur la modélisation de haut niveau basée sur MDE réduit également le temps de conception de 25%.In the last few years, cloud providers and data centers have been integrating FPGAs in their environment for acceleration purpose. This is due to the fact that FPGA based accelerator are known for their lower power and good performance per watt. Moreover, the introduction of the ability for dynamic partial reconfiguration (DPR) of some FPGAs trigger researchers in both industry and academics to propose DPR based virtualized FPGA (vFPGA) cloud services. In most of the existing works, the interconnection between the vFPGAs relies either on BUS or OpenFlow networks. However, both the bus and OpenFlow are not virtualization-aware and optimal solutions. In this thesis, we have proposed a virtualization-aware dynamically scalable NoC for virtualized FPGA accelerators in cloud computing. The NoC components will adapt to the number of active virtualized accelerator dynamically by adding and removing sub-NoCs. To minimize the complexity of NoC architecture design at a low level (HDL implementation), we have proposed a Model-Driven Engineering (MDE) based high-level unified modeling language (UML). A UML/MARTE and IP-XACT based approach are used to define the NoC Topology components at a high-level and generate the required HDL files. Experiment results show that the dynamically scalable NoC can reduce the power consumption by 17%. The MDE based high-level modeling based NoC characterization also reduce the design time by 25%

Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence

7 pages, 9 figuresTracking people in a video sequence is one of the fields of interest in computer vision. It has broad applications in motion capture and surveillance. However, due to the complexity of human dynamic structure, detecting and tracking are not straightforward. Consequently, different detection and tracking techniques with different applications and performance have been developed. To minimize the noise between the prediction and measurement during tracking, Kalman filter has been used as a filtering technique. At the same time, in most cases, detection and tracking results from a single sensor is not enough to detect and track a person. To avoid this problem, using a multi-sensor fusion technique is indispensable. In this paper, a comparative survey of detection, tracking and multi-sensor fusion methods are presented

Comparative survey of visual object classifiers

9 pagesClassification of Visual Object Classes represents one of the most elaborated areas of interest in Computer Vision. It is always challenging to get one specific detector, descriptor or classifier that provides the expected object classification result. Consequently, it critical to compare the different detection, descriptor and classifier methods available and chose a single or combination of two or three to get an optimal result. In this paper, we have presented a comparative survey of different feature descriptors and classifiers. From feature descriptors, SIFT (Sparse & Dense) and HeuSIFT combination colour descriptors; From classification techniques, Support Vector Classifier, K-Nearest Neighbor, ADABOOST, and fisher are covered in comparative practical implementation survey

NoC based virtualized accelerators as cloud services

National audienceHardware accelerators(HwAcc) provide good performance in computation intensive applications. Integrating hardware accelerators in a cloud environment is the optimal way to improve the quality of service. However, mapping all possible application statically into the reconfigurable fabric of the FPGA is rather impractical and prohibitively expensive in terms of resource and power consumption. This problem can be alleviated via time multiplexing the access to the underlying hardware resources, FPGA, by designing dynamically reconfigurable accelerators in the cloud. Similarly, the connection and communication between the accelerators and the reconfigurable control will not be efficient without the use of Network-on-Chip (NoC). In order to address these issues, we propose a NoC based virtualized accelerators for cloud computing. Abstract OBJECTIVES Reconfigurable IPs as a Service (RIPaaS) In this service the user will not have direct access to the FPGA

Run-time Reconfigurable Network-on-chip: a survey

International audienc

NoC based virtualized FPGA as cloud Services

International audienceWeb-based applications are increasingly demanding many computationally intensive services. On the other hand, FPGA-based hardware accelerators(HwAcc) provide good performance in accelerating computationally intensive applications. In addition, some FPGAs support a dynamic partial reconfig-uration (DPR) techniques to virtualize and share the FPGA underlying hardware resources in time multiplexing during run-time to save resource and power consumption. Integrating FPGA in a cloud environment is an indispensable way to improve efficiency and provide acceleration services to demanding users. More importantly, in recent years it was proved that FPGA resources deployed in a cloud environment can be accessed with the same OpenStack software technology used to access virtual machines. However, the performance of the virtualized FPGA is highly dependent on the communication medium used to interconnect the virtualized FPGA resources and the control manager. After analyzing the possible interconnect mediums, we have selected Network-on-Chip (NoC) which support parallel communication as the efficient medium for accelerators. Consequently , we propose a NoC based virtualized FPGA as cloud Services. Two virtualized FPGA-based cloud service: Hardware Accelerator as a Service(HAaaS) and Reconfigurable Region as a Service(RRaaS) are proposed in this paper. The NoC provides layered and parallel communication between the virtualized regions of the FPGA and helps them to communicate their status and exchange data through the routers connected to them. A 2x2-mesh NoC based reconfigurable accelerators for image analysis and matrix computation are implemented and tested showing a promising result for more scalable systems in cloud computing

Sigmoid function based intensity transformation for parameter initialization in MRI-PET Registration Tool for Preclinical Studies

8 pagesImages from Positron Emission Tomography (PET) deliver functional data such as perfusion and metabolism. On the other hand, images from Magnetic Resonance Imaging (MRI) provide information describing anatomical structures. Fusing the complementary information from the two modalities is helpful in oncology. In this project, we implemented a complete tool allowing semi-automatic MRI-PET registration for small animal imaging in the preclinical studies. A two-stage hierarchical registration approach is proposed. First, a global affine registration is applied. For robust and fast registration, principal component analysis (PCA) is used to compute the initial parameters for the global affine registration. Since, only the low intensities in the PET volume reveal the anatomic information on the MRI scan, we proposed a non-uniform intensity transformation to the PET volume to enhance the contrast of the low intensity. This helps to improve the computation of the centroid and principal axis by increasing the contribution of the low intensities. Then, the globally registered image is given as input to the second stage which is a local deformable registration (B-spline registration). Mutual information is used as metric function for the optimization. A multi-resolution approach is used in both stages. The registration algorithm is supported by graphical user interface (GUI) and visualization methods so that the user can interact easily with the process. The performance of the registration algorithm is validated by two medical experts on seven different datasets on abdominal and brain areas including noisy and difficult image volumes

Implémentation en temps réel d'une architecture embarquée pour le comptage de passagers dans le transport public

Colloque national FUTURMOB-1