Real-time visual perception : detection and localisation of static and moving objects from a moving stereo rig

International audienceWe present a novel method for scene reconstruction and moving object detection and tracking, using extensive point tracking (typically more than 4000 points per frame) over time. Current neighbourhood is reconstructed in the form of a 3D point cloud, which allows for extra features (ground detection, path planning, obstacle detection). Reconstruction framework takes moving objects into account, and tracking over time allows for trajectory and speed estimation

Extended occupation grids for non-rigid moving objects tracking

International audienceWe present an evolution of traditional occupancy grid algorithm, based on an extensive probabilistic calculus of the evolution of several variables on a cell neighbourhood. Occupancy, speed and classification are taken into account, the aim being to improve overall perception of an highly changing un- structured environment. Contrary to classical SLAM algorithms, no requisite is made on the amount of rigidity of the scene, and tracking do not rely on geometrical characteristics. We believe that this could have important applications in the automotive field, both from autonomous vehicle and driver assistance, in some areas difficult to address with current algorithms. This article begins with a general presentation of what we aim to do, along with considerations over traditional occupancy grids limits and their reasons. We will then present our proposition, and detail some of its key aspects, namely update rules and perfor- mance consequences. A second part will be more practical, and will begin with a brief presentation of the GPU implementation of the algorithm, before turning to sensor models and some results

Proposition for propagated occupation grids for non-rigid moving objects tracking

International audienceAutonomous navigation among humans is, however simple it might seems, a difficult subject which draws a lot a attention in our days of increasingly autonomous systems. From a typical scene from a human environment, diverse shapes, behaviours, speeds or colours can be gathered by a lot of sensors ; and a generic mean to perceive space and dynamics is all the more needed, if not easy. We propose an incremental evolution over the well-known occupancy grid paradigm, introducing grid cell propagation over time and a limited neighbourhood, handled by probabilistic calculus. Our algorithm runs in real-time from a GPU implementation, and considers completely generically space-cells propagation, without any a priori requirements. It produces a set of belief maps of our environment, handling occupancy, but also items dynamics, relative rigidity links, and an initial object classification. Observations from free-space sensors are thus turned into information needed for autonomous navigation

Proposition for propagated occupation grids for non-rigid moving objects tracking

International audienceAutonomous navigation among humans is, however simple it might seems, a difficult subject which draws a lot a attention in our days of increasingly autonomous systems. From a typical scene from a human environment, diverse shapes, behaviours, speeds or colours can be gathered by a lot of sensors ; and a generic mean to perceive space and dynamics is all the more needed, if not easy. We propose an incremental evolution over the well-known occupancy grid paradigm, introducing grid cell propagation over time and a limited neighbourhood, handled by probabilistic calculus. Our algorithm runs in real-time from a GPU implementation, and considers completely generically space-cells propagation, without any a priori requirements. It produces a set of belief maps of our environment, handling occupancy, but also items dynamics, relative rigidity links, and an initial object classification. Observations from free-space sensors are thus turned into information needed for autonomous navigation

Track-to-Track Fusion Using Split Covariance Intersection Filter-Information Matrix Filter (SCIF-IMF) for Vehicle Surrounding Environment Perception

International audienceVehicle surrounding environment perception is an important process for many applications. Nowadays, a tendency is to incorporate redundant and complementary sensors into an intelligent vehicle, in order to enhance its perception ability; then an essential issue arises naturally, i.e. what fusion architecture can be used to combine the data from multiple sensors? In this paper, we propose a new track-totrack fusion architecture using the split covariance intersection filter-information matrix filter (SCIF-IMF). The basic idea is to use the IMF (adapted for estimates in split form) to handle the track temporal correlation of each sensor system and to use the SCIF to handle track spatial correlation. The proposed architecture enjoys complete sensor modularity and thus enables flexible self-adjustment. A simulation based comparative study is presented, which shows that the track-totrack fusion architecture using the SCIF-IMF can achieve centralized architecture comparable performance

Track-to-track fusion using split covariance intersection filter-information matrix filter (SCIF-IMF) for vehicle surrounding environment perception

International audienceVehicle surrounding environment perception is an important process for many applications. Nowadays, a tendency is to incorporate redundant and complementary sensors into an intelligent vehicle, in order to enhance its perception ability; then an essential issue arises naturally, i.e. what fusion architecture can be used to combine the data from multiple sensors? In this paper, we propose a new track-totrack fusion architecture using the split covariance intersection filter-information matrix filter (SCIF-IMF). The basic idea is to use the IMF (adapted for estimates in split form) to handle the track temporal correlation of each sensor system and to use the SCIF to handle track spatial correlation. The proposed architecture enjoys complete sensor modularity and thus enables flexible self-adjustment. A simulation based comparative study is presented, which shows that the track-totrack fusion architecture using the SCIF-IMF can achieve centralized architecture comparable performance

A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED

RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity

A multiple view polarimetric camera

A multiple view polarimetric camera is developed. The system uses four separate action cameras and software is employed to map the images onto each other in order to generate the Stokes vectors, the degree of linear polarisation and angle images. To ensure robustness, an automated calibration system has been developed that ensures the pixels are correctly mapped. Video frame synchronisation is also developed

Detection of capillary abnormalities in early diabetic retinopathy using scanning laser ophthalmoscopy and optical coherence tomography combined with adaptive optics

This study tested if a high-resolution, multi-modal, multi-scale retinal imaging instrument can provide novel information about structural abnormalities in vivo. The study examined 11 patients with very mild to moderate non-proliferative diabetic retinopathy (NPDR) and 10 healthy subjects using fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), adaptive optics scanning laser ophthalmoscopy (AO-SLO), adaptive optics OCT and OCTA (AO-OCT(A)). Of 21 eyes of 11 patients, 11 had very mild NPDR, 8 had mild NPDR, 2 had moderate NPDR, and 1 had no retinopathy. Using AO-SLO, capillary looping, inflections and dilations were detected in 8 patients with very mild or mild NPDR, and microaneurysms containing hyperreflective granular elements were visible in 9 patients with mild or moderate NPDR. Most of the abnormalities were seen to be perfused in the corresponding OCTA scans while a few capillary loops appeared to be occluded or perfused at a non-detectable flow rate, possibly because of hypoperfusion. In one patient with moderate NPDR, non-perfused capillaries, also called ghost vessels, were identified by alignment of corresponding en face AO-OCT and AO-OCTA images. The combination of multiple non-invasive imaging methods could identify prominent microscopic abnormalities in diabetic retinopathy earlier and more detailed than conventional fundus imaging devices.</p