Generating Code and Memory Buffers to Reorganize Data on Many-core Architectures

International audienceThe dataflow programming model has shown to be a relevant approach to efficiently run mas-sively parallel applications over many-core architectures. In this model, some particular builtin agents are in charge of data reorganizations between user agents. Such agents can Split, Join and Duplicate data onto their communication ports. They are widely used in signal processing for example. These system agents, and their associated implementations, are of major impor-tance when it comes to performance, because they can stand on the critical path (think about Amdhal's law). Furthermore, a particular data reorganization can be expressed by the devel-oper in several ways that may lead to inefficient solutions (mostly unneeded data copies and transfers). In this paper, we propose several strategies to manage data reorganization at compile time, with a focus on indexed accesses to shared buffers to avoid data copies. These strategies are complementary: they ensure correctness for each system agent configuration, as well as performance when possible. They have been implemented within the Sigma-C industry-grade compilation toolchain and evaluated over the Kalray MPPA 256-core processor

Registration of retinal images by a MAS-ICP approach - a preliminary study

Diabetic retinopathy has been revealed as the most common cause of blindness among people of working age. For monitoring the pathology image registration algorithms applied to retinal images is very useful. In this work, a novel vessel-based retinal image registration approach is proposed. The segmentation of the vasculature is performed by a multi-agent system model. All these information is then used in a Robust Point Matching Iterative Closest Point algorithm improved by a Region Bootstrap approach. With this preliminary study, the novelty of integrating all these algorithms for image registration preceded by a multi-agents system for image edges detection seems to be efficient for temporal retinal image registration. Consequently, a system developed on basis of this approach could help in screening programs for the diabetic retinopathy prevention.C. P. thanks the Fundacao para a Ciencia e Tecnologia (FCT), Portugal for the Ph.D. Grant SFRH/BD/ 61829/2009

Random excitation by optimized pulse inversion in contrast harmonic imaging

ISBN: 978-2-919340-01-9 EAN: 9782919340019International audienceOver the past twenty years, in ultrasound contrast imaging, new physiological information are obtained by the detection of non-linearities generated by the microbubbles. One of the most used techniques is the pulse inversion imaging. The usual command of this system is a fixed-frequency sinus wave. An optimal choice of this command requires the knowledge of the transducer and of the medium to obtain the best contrast-to-tissue ratio. However, these information are experimentally inaccessible. Our goal is to seek the command which maximizes the contrast-to-tissue ratio. Among several noises, we identified the one which maximized the contrast-to-tissue ratio. A new suboptimal control was made from the parameters of a nonlinear autoregressive filter and from suboptimal noise. The contrast-to-tissue ratio was then iteratively optimized by the method of Nelder-Mead which adjusted the filter parameters. The gain compared to the case in which we used at the optimal frequency can reach about 1 dB and 5 dB in comparison to the center frequency of the transducer. By adding a closed loop, the system automatically proposes the optimal command without any a priori knowledge of the system or of the medium explored and without any hypothesis about the shape of the command

Analysis and modelling of the Optimal Command for a Ultrasound Pulse Inversion Imaging System

International audienceOver the past twenty years, in ultrasound imaging, contrast and resolution were improved by using the nonlinearties of the medium. One of the most common techniques which used this properties is the pulse inversion imaging. The optimization of this imaging system that we proposed has consisted in finding the optimal command. However, the properties which enable to make an optimal command was not known and that is why we seek the best optimal command by exciting the system by random sequences. In this study, we proposed two steps in our analysis: an analysis and a modelling stage. The proposed model took into account the nonlinearity of the optimal command and enabled to describe the optimal command by using some parameters. If the synthetic model was used in the pulse inversion imaging system, the contrast can reach the same performances

Method for 3D modelling based on structure from motion processing of sparse 2D images

A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parameters

Topology Reasoning for Driving Scenes

Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assignment relationship between lanes and traffic elements, where a comprehensive topology reasoning method is vacant. On one hand, previous map learning techniques struggle in deriving lane connectivity with segmentation or laneline paradigms; or prior lane topology-oriented approaches focus on centerline detection and neglect the interaction modeling. On the other hand, the traffic element to lane assignment problem is limited in the image domain, leaving how to construct the correspondence from two views an unexplored challenge. To address these issues, we present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. To capture the driving scene topology, we introduce three key designs: (1) an embedding module to incorporate semantic knowledge from 2D elements into a unified feature space; (2) a curated scene graph neural network to model relationships and enable feature interaction inside the network; (3) instead of transmitting messages arbitrarily, a scene knowledge graph is devised to differentiate prior knowledge from various types of the road genome. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2, where our approach outperforms all previous works by a great margin on all perceptual and topological metrics. The code would be released soon

ПОИСК СТРУКТУРНЫХ ОСОБЕННОСТЕЙ СТРОЕНИЯ ТОМОГРАФИЧЕСКИХ ИЗОБРАЖЕНИЙ НА ОСНОВЕ КОНЦЕПЦИИ АКТИВНЫХ АГЕНТОВ

Предлагается алгоритм поиска особенностей строения томографических изображений, основанный на концепции самоорганизующихся агентных систем. В реализованной мультиагентной системе поставленная задача решается коллективно за счет конкуренции автономных агентов двух типов. Работа мультиагентного алгоритма демонстрируется на примере задачи поиска закономерностей строения, связанных с туберкулезом легких. Эффективность предложенного алгоритма оценивается на достаточно большой базе данных трехмерных КТ-изображений, включающей томограммы грудной клетки 111 пациентов общим объемом около 10 000 слоев

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP proble