Bag-of-word based brand recognition using Markov Clustering Algorithm for codebook generation

International audienceIn order to address the issue of counterfeiting online, it is necessary to use automatic tools that analyze the large amount of information available over the Internet. Analysis methods that extract information about the content of the images are very promising for this purpose. In this paper, a method that automatically extract the brand of objects in images is proposed. The method does not explicitly search for text or logos. This information is implicitly included in the Bag-of-Words representation. In the Bag-of-Words paradigm, visual features are clustered to create the visual words. Despite its shortcomings, k-means is the most widely used algorithm. With k-means, the selection of the number of visual words is critical. In this paper, another clustering algorithm is proposed. Markov Cluster Algorithm (MCL) is very fast, does not require an arbitrary selection of the number of classes and does not rely on random initialization. First, we demonstrate in this paper that MCL is competitive to k-means with a number of cluster experimentally selected. Second, we show that it is possible to identify brand from objects in images without previous knowledge about visual identity of these brands

An analysis of the extent to which monetary and non-monetary rewards motivate employees within a Chinese Financial-services SME

There have been many controversies related to the importance of monetary and non-monetary rewards on motivating employees. In order to help managers to motivate employees effectively, this research set up to analyze the extent to which monetary and non-monetary rewards motivate employees within a case company that is a Chinese Financial-services SME

WiseEye: A Platform to Manage and Experiment on Smart Camera Networks

International audienceEmbedded vision is probably at the edge of phenomenal expansion. The smart cameras are embedding some processing units which are more and more powerful. Last decade, high-speed image processing can be implemented on specifically designed architectures [1] nevertheless the designing time of such systems was quite high and time to market therefore as well. Since, powerful chips (i.e System On Chip) and quick prototyping methodologies are contently emerging [2],[3],[4] and enable more complex algorithms to be implemented faster. Moreover, smart cameras which are embedding flexible and powerful multi-core processors or Graphic Processors Unit (GPU) are now available and can be considered as well as a solution to implement faster some complex image processing algorithms. The smart camera can be considered as a powerful sensor which enables very complex information to be extracted in real-time from the video scene. Using several cameras simultaneously and dealing with a multi-view configuration is even more challenging but enable more information to be available. Therefore, we present in this paper a platform, named WiseEye, to manage and experiment on a smart camera network based on low-cost multi-core processors. A network of low-cost multi-core processors has been deployed. We have already developed a framework to ease application development and debugging [5]. The framework aims to offer a high degree of flexibility for managing the tasks and the memory allocation. Hence, the framework enables the priority and the allocation of each task to be controlled. The image acquisition task can be completely independent from the other processing tasks. The framework includes an interface dedicated to the management of the tasks: the user can add or suppress a task during the runtime, logs or processing results can be visualised for each task. Smart cameras use a dedicated network configuration and service providing tool named pyM2SL (python Mesh and Multicast Services for Linux) which has been developed in le2i. pyM2SL allows dynamic application management and configuration from a master node on the network, and service deployment and discovery at runtime. Services can be anything from video stream to processing resources, each allocable according to the user's requirements. pyM2SL is a free software, publicly available [6] under AGPL v3 open source license. The viewer application has been designed using multi-platform libraries only, it is based on a Qt GUI. The received video streams can be decoded with a software decoder or with hardware to reduce the CPU usage. The video streaming is based on GStreamer which offers high performances (TCP or UDP protocols available) with very low latencies. We are currently investigating on the real-time implementation of complex image processing on this kind of hardware targets to provide extra services and security to the people living in an environment equipped with a smart camera network. Different targeted applications are then finally presented such as human tracking [7] for smart building management, control access based on a multi-modal approach, real-time fall detection [4] or recent remote physiological measurements (i.e. heart rate) based on video imaging [8]

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Détection de la présence humaine par vision

The work presented in this manuscript deals with people detection and activity analysis in images sequences. This work has been done in the PRISME institut within the framework of the CAPTHOM project of the French Cluster S2E2. After a state of the art on video analysis and a comparative study of several video surveillance tools, we present the people detection method proposed within the framework of the CAPTHOM project. This method is based on three steps : change detection, mobile objects tracking and classification. Each steps is described in this thesis. The system was assessed on a wide videos dataset. Then, we present methods used to obtain other high-level information concerning the activity of detected persons. A criterion for characterizing their activity is presented. Then, a multi-sensors stereovision system combining an infrared and a daylight camera is used to increase performances of the people detection system but also to localize persons in the 3D space and so build the moving cartography. Finally, an abnormal events detection method based on statistics about spatio-temporal foreground pixel distribution is presented. These proposed methods offer robust and efficient solutions on high-level information extraction from images sequences.Les travaux présentés dans ce manuscrit traitent de la détection de personnes dans des séquences d’images et de l’analyse de leur activité. Ces travaux ont été menés au sein de l’institut PRISME dans le cadre du projet CAPTHOM du pôle de compétitivité S2E2. Après un état de l’art sur l’analyse de séquences d’images pour l’interprétation automatique de scènes et une étude comparative de modules de vidéo-surveillance, nous présentons la méthode de détection de personnes proposée dans le cadre du projet CAPTHOM. Celle-ci s’articule autour de trois étapes : la détection de changement, le suivi d’objets mobiles et la classification. Chacune de ces étapes est décrite dans ce manuscrit. Ce système a été évalué sur une large base de vidéos correspondant à des scénarios de cas d’usage de CAPTHOM établis par les partenaires du projet. Ensuite, nous présentons des méthodes permettant d’obtenir, à partir du flux vidéo d’une ou deux caméras, d’autres informations de plus haut-niveau sur l’activité des personnes détectées. Nous présentons tout d’abord une mesure permettant de quantifier leur activité. Ensuite, un système de stéréovision multi-capteurs combinant une caméra infrarouge et une caméra visible est utilisé pour augmenter les performances du système de détection mais aussi pour permettre la localisation dans l’espace des personnes et donc accéder à une cartographie de leurs déplacements. Finalement, une méthode de détection d’événements anormaux, basée sur des statistiques de distributions spatiales et temporelles des pixels de l’avant-plan est détaillée. Les méthodes proposées offrent un panel de solutions performantes sur l’extraction d’informations haut-niveau à partir de séquences d’images

Human detection using computer vision

Les travaux présentés dans ce manuscrit traitent de la détection de personnes dans des séquences d’images et de l’analyse de leur activité. Ces travaux ont été menés au sein de l’institut PRISME dans le cadre du projet CAPTHOM du pôle de compétitivité S2E2. Après un état de l’art sur l’analyse de séquences d’images pour l’interprétation automatique de scènes et une étude comparative de modules de vidéo-surveillance, nous présentons la méthode de détection de personnes proposée dans le cadre du projet CAPTHOM. Celle-ci s’articule autour de trois étapes : la détection de changement, le suivi d’objets mobiles et la classification. Chacune de ces étapes est décrite dans ce manuscrit. Ce système a été évalué sur une large base de vidéos correspondant à des scénarios de cas d’usage de CAPTHOM établis par les partenaires du projet. Ensuite, nous présentons des méthodes permettant d’obtenir, à partir du flux vidéo d’une ou deux caméras, d’autres informations de plus haut-niveau sur l’activité des personnes détectées. Nous présentons tout d’abord une mesure permettant de quantifier leur activité. Ensuite, un système de stéréovision multi-capteurs combinant une caméra infrarouge et une caméra visible est utilisé pour augmenter les performances du système de détection mais aussi pour permettre la localisation dans l’espace des personnes et donc accéder à une cartographie de leurs déplacements. Finalement, une méthode de détection d’événements anormaux, basée sur des statistiques de distributions spatiales et temporelles des pixels de l’avant-plan est détaillée. Les méthodes proposées offrent un panel de solutions performantes sur l’extraction d’informations haut-niveau à partir de séquences d’images.The work presented in this manuscript deals with people detection and activity analysis in images sequences. This work has been done in the PRISME institut within the framework of the CAPTHOM project of the French Cluster S2E2. After a state of the art on video analysis and a comparative study of several video surveillance tools, we present the people detection method proposed within the framework of the CAPTHOM project. This method is based on three steps : change detection, mobile objects tracking and classification. Each steps is described in this thesis. The system was assessed on a wide videos dataset. Then, we present methods used to obtain other high-level information concerning the activity of detected persons. A criterion for characterizing their activity is presented. Then, a multi-sensors stereovision system combining an infrared and a daylight camera is used to increase performances of the people detection system but also to localize persons in the 3D space and so build the moving cartography. Finally, an abnormal events detection method based on statistics about spatio-temporal foreground pixel distribution is presented. These proposed methods offer robust and efficient solutions on high-level information extraction from images sequences

Remote Photoplethysmography Based on Implicit Living Skin Tissue Segmentation

International audienceRegion of interest selection is an essential part for remote photoplethysmography (rPPG) algorithms. Most of the time, face detection provided by a supervised learning of physical appearance features coupled with skin detection is used for region of interest selection. However, both methods have several limitations and we propose to implicitly select living skin tissue via their particular pulsatility feature. The input video stream is decomposed into several temporal superpixels from which pulse signals are extracted. Pulsatility measure for each temporal superpixel is then used to merge pulse traces and estimate the photoplethysmogram signal. This allows to select skin tissue and furthermore to favor areas where the pulse trace is more predominant. Experimental results showed that our method perform better than state of the art algorithms without any critical face or skin detection

Détection de la présence humaine par vision

Les travaux présentés dans ce manuscrit traitent de la détection de personnes dans des séquences d images et de l analyse de leur activité. Ces travaux ont été menés au sein de l institut PRISME dans le cadre du projet CAPTHOM du pôle de compétitivité S2E2. Après un état de l art sur l analyse de séquences d images pour l interprétation automatique de scènes et une étude comparative de modules de vidéo-surveillance, nous présentons la méthode de détection de personnes proposée dans le cadre du projet CAPTHOM. Celle-ci s articule autour de trois étapes : la détection de changement, le suivi d objets mobiles et la classification. Chacune de ces étapes est décrite dans ce manuscrit. Ce système a été évalué sur une large base de vidéos correspondant à des scénarios de cas d usage de CAPTHOM établis par les partenaires du projet. Ensuite, nous présentons des méthodes permettant d obtenir, à partir du flux vidéo d une ou deux caméras, d autres informations de plus haut-niveau sur l activité des personnes détectées. Nous présentons tout d abord une mesure permettant de quantifier leur activité. Ensuite, un système de stéréovision multi-capteurs combinant une caméra infrarouge et une caméra visible est utilisé pour augmenter les performances du système de détection mais aussi pour permettre la localisation dans l espace des personnes et donc accéder à une cartographie de leurs déplacements. Finalement, une méthode de détection d événements anormaux, basée sur des statistiques de distributions spatiales et temporelles des pixels de l avant-plan est détaillée. Les méthodes proposées offrent un panel de solutions performantes sur l extraction d informations haut-niveau à partir de séquences d images.The work presented in this manuscript deals with people detection and activity analysis in images sequences. This work has been done in the PRISME institut within the framework of the CAPTHOM project of the French Cluster S2E2. After a state of the art on video analysis and a comparative study of several video surveillance tools, we present the people detection method proposed within the framework of the CAPTHOM project. This method is based on three steps : change detection, mobile objects tracking and classification. Each steps is described in this thesis. The system was assessed on a wide videos dataset. Then, we present methods used to obtain other high-level information concerning the activity of detected persons. A criterion for characterizing their activity is presented. Then, a multi-sensors stereovision system combining an infrared and a daylight camera is used to increase performances of the people detection system but also to localize persons in the 3D space and so build the moving cartography. Finally, an abnormal events detection method based on statistics about spatio-temporal foreground pixel distribution is presented. These proposed methods offer robust and efficient solutions on high-level information extraction from images sequences.ORLEANS-SCD-Bib. electronique (452349901) / SudocSudocFranceF

Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

International audienceRemote Photoplethysmography (rPPG) enables quantifying blood volume variations in the skin tissues from an input video recording, using a regular RGB camera. Obtained pulse signals often contain noisy portions due to motion, leading researchers to put aside a great number of rPPG signals in their studies. In this paper, an approach using a Gated Recurrent Unit-based neural network model in order to identify reliable portions in rPPG signals is proposed. This is done by classifying rPPG signal samples into reliable and unreliable samples. For this purpose, rPPG and electrocardiography signals (ECG) were collected from 11 participants, rPPG signal samples were labeled (ECG was used as ground truth), and data were augmented to reach a total number of 11000 1-minute-long rPPG signals. We developed a model composed of a unidimensional CNN and a Bidirectional GRU (1D-CNN+B-GRU) for this study, and obtained an accuracy rate of 85.88%