Pratique anesthésique à Lubumbashi: indications, types de chirurgie et types de patient

Introduction: Cette étude a pour objectif de décrire la pratique anesthésique dans un pays à faible revenu et où le plateau technique anesthésique est moins équipé. Méthodes: Une étude descriptive transversale a été menée durant l'année 2013. L'enquête a concerné les pratiques anesthésiques, les indications chirurgicales et les caractéristiques des malades. L'encodage et l'analyse des données ont été réalisées grâce aux logiciels Epi Info 3.5.3 et Excel 2010. Résultats: Nous avons enregistré 2358 patients dont l'âge médian était de 29 + 15 ans, avec 81,5% âgés de 11 à 50 ans. Parmi eux, 67,3% des malades étaient du sexe féminin. Dans ensemble, 62,5% de ces patients étaient pris en charge pour les interventions programmées. L'évaluation du risque anesthésique a montré que 91,9% des patients étaient de la classe ASA I et II. La chirurgie la plus pratiquée était viscérale (46,7%) suivie de la chirurgie gynéco-obstétricale (29,2%). Les différents types d'anesthésie étaient les suivants: anesthésie générale (87,6%), locorégionale (11,8%) et combinée (0,6%). Conclusion: La pratique anesthésique dans la population d'étude était dominée par l'anesthésie générale. Les malades étaient au trois quart de sexe féminin et de la classe ASA I et II. Les résultats de cette étude indiquent la nécessité d'évaluer l'issue de cette pratique. La pratique anesthésique à Lubumbashi est tributaire du plateau technique, es compétences du personnel et de l'acceptabilité du type d'anesthésie par les patients

Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers

This paper improves state-of-the-art visual object trackers that use online adaptation. Our core contribution is an offline meta-learning-based method to adjust the initial deep networks used in online adaptation-based tracking. The meta learning is driven by the goal of deep networks that can quickly be adapted to robustly model a particular target in future frames. Ideally the resulting models focus on features that are useful for future frames, and avoid overfitting to background clutter, small parts of the target, or noise. By enforcing a small number of update iterations during meta-learning, the resulting networks train significantly faster. We demonstrate this approach on top of the high performance tracking approaches: tracking-by-detection based MDNet and the correlation based CREST. Experimental results on standard benchmarks, OTB2015 and VOT2016, show that our meta-learned versions of both trackers improve speed, accuracy, and robustness.Comment: Code: https://github.com/silverbottlep/meta_tracker

Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking

Visual object tracking is one of the major challenges in the field of computer vision. Correlation Filter (CF) trackers are one of the most widely used categories in tracking. Though numerous tracking algorithms based on CFs are available today, most of them fail to efficiently detect the object in an unconstrained environment with dynamically changing object appearance. In order to tackle such challenges, the existing strategies often rely on a particular set of algorithms. Here, we propose a robust framework that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation. We also supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map. Further, we demonstrate the impact of displacement consistency on CF trackers. The generality and efficiency of the proposed framework is illustrated by integrating our contributions into two state-of-the-art CF trackers: SRDCF and ECO. As per the comprehensive experiments on the VOT2016 dataset, our top trackers show substantial improvement of 14.7% and 6.41% in robustness, 11.4% and 1.71% in Average Expected Overlap (AEO) over the baseline SRDCF and ECO, respectively.Comment: Published in ACCV 201

Online, Real-Time Tracking Using a Category-to-Individual Detector

A method for online, real-time tracking of objects is presented. Tracking is treated as a repeated detection problem where potential target objects are identified with a pre-trained category detector and object identity across frames is established by individual-specific detectors. The individual detectors are (re-)trained online from a single positive example whenever there is a coincident category detection. This ensures that the tracker is robust to drift. Real-time operation is possible since an individual-object detector is obtained through elementary manipulations of the thresholds of the category detector and therefore only minimal additional computations are required. Our tracking algorithm is benchmarked against nine state-of-the-art trackers on two large, publicly available and challenging video datasets. We find that our algorithm is 10% more accurate and nearly as fast as the fastest of the competing algorithms, and it is as accurate but 20 times faster than the most accurate of the competing algorithms

Occlusion and Motion Reasoning for Long-Term Tracking

International audienceObject tracking is a reoccurring problem in computer vision. Tracking-by-detection approaches, in particular Struck (Hare et al., 2011), have shown to be competitive in recent evaluations. However, such approaches fail in the presence of long-term occlusions as well as severe viewpoint changes of the object. In this paper we propose a principled way to combine occlusion and motion reasoning with a tracking-by-detection approach. Occlusion and motion reasoning is based on state-of-the-art long-term trajectories which are labeled as object or background tracks with an energy-based formulation. The overlap between labeled tracks and detected regions allows to identify occlusions. The motion changes of the object between consecutive frames can be estimated robustly from the geometric relation between object trajectories. If this geometric change is significant, an additional detector is trained. Experimental results show that our tracker obtains state-of-the-art results and handles occlusion and viewpoints changes better than competing tracking methods

Online Learning for 3D LiDAR-based Human Detection: Experimental Analysis of Point Cloud Clustering and Classification Methods

This paper presents a system for online learning of human classifiers by mobile service robots using 3D~LiDAR sensors, and its experimental evaluation in a large indoor public space. The learning framework requires a minimal set of labelled samples (e.g. one or several samples) to initialise a classifier. The classifier is then retrained iteratively during operation of the robot. New training samples are generated automatically using multi-target tracking and a pair of "experts" to estimate false negatives and false positives. Both classification and tracking utilise an efficient real-time clustering algorithm for segmentation of 3D point cloud data. We also introduce a new feature to improve human classification in sparse, long-range point clouds. We provide an extensive evaluation of our the framework using a 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments demonstrate the influence of the system components and improved classification of humans compared to the state-of-the-art

DS-KCF: a real-time tracker for RGB-D data

© 2016 The Author(s) We propose an RGB-D single-object tracker, built upon the extremely fast RGB-only KCF tracker that is able to exploit depth information to handle scale changes, occlusions, and shape changes. Despite the computational demands of the extra functionalities, we still achieve real-time performance rates of 35–43 fps in MATLAB and 187 fps in our C++ implementation. Our proposed method includes fast depth-based target object segmentation that enables, (1) efficient scale change handling within the KCF core functionality in the Fourier domain, (2) the detection of occlusions by temporal analysis of the target’s depth distribution, and (3) the estimation of a target’s change of shape through the temporal evolution of its segmented silhouette allows. Finally, we provide an in-depth analysis of the factors affecting the throughput and precision of our proposed tracker and perform extensive comparative analysis. Both the MATLAB and C++ versions of our software are available in the public domain

Multifactorial Origins of Heart and Gut Defects in nipbl-Deficient Zebrafish, a Model of Cornelia de Lange Syndrome

nipbl-deficient zebrafish provide evidence that heart and gut defects in Cornelia de Lange Syndrome are caused by combined effects of multiple gene expression changes that occur during early embryonic development