Colour displays for categorical images

We propose a method for identifying a set of colours for displaying 2-D and 3-D categorical images when the categories are unordered labels. The principle is to find maximally distinct sets of colours. We either generate colours sequentially, to maximise the dissimilarity or distance between a new colour and the set of colours already chosen, or use a simulated annealing algorithm to find a set of colours of specified size. In both cases, we use a Euclidean metric on the perceptual colour space, CIE-LAB, to specify distances

Color quantization and image analysis

The aim of this paper is to provide an up-to-date review of the numerous aspects and technics of color quantization used in image analysis. This synthesis is all the more necessary that this field of study is in high expansion . In this article, we also propose several criteria and study parameters linked to visual analysis in order to improve the existing color quantization methods or to define new more accurate methods.L'objectif de cet article est de faire une synthèse sur les multiples aspects et techniques de quantification couleur développés en analyse d'image. Cette synthèse s'avère d'autant plus nécessaire que cette voix de recherche est en plein essor et que de multiples techniques peuvent être utilisées. Cet article propose également plusieurs critères et paramètres d'étude, fondés sur l'analyse visuelle, afin d'améliorer les méthodes de quantification couleur existantes ou de définir de nouvelles méthodes plus pertinentes

Segmentation de scènes extérieures à partir d'ensembles d'étiquettes à granularité et sémantique variables

International audienceIn this work, we present an approach that leverages multiple datasets annotated using different classes (different labelsets) to improve the classification accuracy on each individual dataset. We focus on semantic full scene labeling of outdoor scenes. To achieve our goal, we use the KITTI dataset as it illustrates very well the focus of our paper : it has been sparsely labeled by multiple research groups over the past few years but the semantics and the granularity of the labels differ from one set to another. We propose a method to train deep convolutional networks using multiple datasets with potentially inconsistent labelsets and a selective loss function to train it with all the available labeled data while being reliant to inconsistent labelings. Experiments done on all the KITTI dataset's labeled subsets show that our approach consistently improves the classification accuracy by exploiting the correlations across data-sets both at the feature level and at the label level.Ce papier présente une approche permettant d'utiliser plusieurs bases de données annotées avec différents ensembles d'étiquettes pour améliorer la précision d'un classifieur entrainé sur une tâche de segmentation sémantique de scènes extérieures. Dans ce contexte, la base de données KITTI nous fournit un cas d'utilisation particulièrement pertinent : des sous-ensembles distincts de cette base ont été annotés par plusieurs équipes en utilisant des étiquettes différentes pour chaque sous-ensemble. Notre méthode permet d'entraîner un réseau de neurones convolutionnel (CNN) en utilisant plusieurs bases de données avec des étiquettes possiblement incohérentes. Nous présentons une fonction de perte sélective pour entrainer ce réseau et plusieurs approches de fusion permettant d'exploiter les corrélations entre les différents ensembles d'étiquettes. Le réseau utilise ainsi toutes les données disponibles pour améliorer les performances de classification sur chaque ensemble. Les expériences faites sur les différents sous-ensembles de la base de données KITTI montrent comment chaque proposition contribue à améliorer le classifieur

A Comparison of Embedded Deep Learning Methods for Person Detection

Recent advancements in parallel computing, GPU technology and deep learning provide a new platform for complex image processing tasks such as person detection to flourish. Person detection is fundamental preliminary operation for several high level computer vision tasks. One industry that can significantly benefit from person detection is retail. In recent years, various studies attempt to find an optimal solution for person detection using neural networks and deep learning. This study conducts a comparison among the state of the art deep learning base object detector with the focus on person detection performance in indoor environments. Performance of various implementations of YOLO, SSD, RCNN, R-FCN and SqueezeDet have been assessed using our in-house proprietary dataset which consists of over 10 thousands indoor images captured form shopping malls, retails and stores. Experimental results indicate that, Tiny YOLO-416 and SSD (VGG-300) are the fastest and Faster-RCNN (Inception ResNet-v2) and R-FCN (ResNet-101) are the most accurate detectors investigated in this study. Further analysis shows that YOLO v3-416 delivers relatively accurate result in a reasonable amount of time, which makes it an ideal model for person detection in embedded platforms

Isolation of Angola-like Marburg virus from Egyptian rousette bats from West Africa.

Marburg virus (MARV) causes sporadic outbreaks of severe Marburg virus disease (MVD). Most MVD outbreaks originated in East Africa and field studies in East Africa, South Africa, Zambia, and Gabon identified the Egyptian rousette bat (ERB; Rousettus aegyptiacus) as a natural reservoir. However, the largest recorded MVD outbreak with the highest case-fatality ratio happened in 2005 in Angola, where direct spillover from bats was not  shown. Here, collaborative studies by the Centers for Disease Control and Prevention, Njala University, University of California, Davis USAID-PREDICT, and the University of Makeni identify MARV circulating in ERBs in Sierra Leone. PCR, antibody and virus isolation data from 1755 bats of 42 species shows active MARV infection in approximately 2.5% of ERBs. Phylogenetic analysis identifies MARVs that are similar to the Angola strain. These results provide evidence of MARV circulation in West Africa and demonstrate the value of pathogen surveillance to identify previously undetected threats