Comprehensive review of vision-based fall detection systems

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers

Mapping Emotional Cartography

Maps are somehow shy. They tend to hide their emotional side behind their clear lines, precise points, minimalistic words, numerical data and informative purpose. But when we scratch the cartographic surface, maps appear to be impregnated with all sorts of emotions. The emotions associated with the topic mapped and the ones evoked through the cartographic design. The emotions felt by the mapmaker while drawing the map and the ones felt by the map user when discovering it. The anger and sadness triggered by social injustices revealed on a map, or the simple pleasure felt while admiring a beautiful cartographic design. The emotional experiences we clearly remember and the most common ones we hardly notice or we simply forget. Beneath the surface, maps and mapping teem with emotions of all sorts. In this introduction to the special issue on Maps and Emotions, we will reveal the multiple relationships that exist between maps, mapping, and emotions

Visual Object Tracking Approach Based on Wavelet Transforms

In this Thesis, a new visual object tracking (VOT) approach is proposed to overcome the main challenging problem encountered within the existing approaches known as the significant appearance changes which is due mainly to the heavy occlusion and illumination variations. Indeed, the proposed approach is based on combining the deep convolutional neural networks (CNN), the histograms of oriented gradients (HOG) features, and the discrete wavelet packet transform to ensure the implementation of three ideas. Firstly, solving the problem of illumination variation by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transform is used at the output for extracting the CNN features. Secondly, by combining four learned correlation filters with convolutional features, the target location is deduced using multichannel correlation maps at the CNNs output. On the other side, the maximum value of the resulting maps from correlation filters with convolutional features produced by HOG feature of the image template previously obtained are calculated and which are used as an updating parameter of the correlation filters extracted from CNN and from HOG where the major aim is to ensure long-term memory of target appearance so that the target item may be recovered if tracking fails. Thirdly, to increase the performance of HOG, the coefficients of the discrete packet wavelet transform are employed instead of the image template. Finally, for the validation and the evaluation of the proposed tracking approach performance based on specific performance metrics in comparison to the state-of-the-art counterparts, extensive simulation experiments on benchmark datasets have been conducted out, such as OTB50, OTB100 , TC128 ,and UAV20. The obtained results clearly prove the validity of the proposed approach in solving the encountered problems of visual object tracking in almost the experiment cases presented in this thesis compared to other existing tracking approaches

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Calibration-free Pedestrian Partial Pose Estimation Using a High-mounted Kinect

Les applications de l’analyse du comportement humain ont subit de rapides développements durant les dernières décades, tant au niveau des systèmes de divertissements que pour des applications professionnelles comme les interfaces humain-machine, les systèmes d’assistance de conduite automobile ou des systèmes de protection des piétons. Cette thèse traite du problème de reconnaissance de piétons ainsi qu’à l’estimation de leur orientation en 3D. Cette estimation est faite dans l’optique que la connaissance de cette orientation est bénéfique tant au niveau de l’analyse que de la prédiction du comportement des piétons. De ce fait, cette thèse propose à la fois une nouvelle méthode pour détecter les piétons et une manière d’estimer leur orientation, par l’intégration séquentielle d’un module de détection et un module d’estimation d’orientation. Pour effectuer cette détection de piéton, nous avons conçu un classificateur en cascade qui génère automatiquement une boîte autour des piétons détectés dans l’image. Suivant cela, des régions sont extraites d’un nuage de points 3D afin de classifier l’orientation du torse du piéton. Cette classification se base sur une image synthétique grossière par tramage (rasterization) qui simule une caméra virtuelle placée immédiatement au-dessus du piéton détecté. Une machine à vecteurs de support effectue la classification à partir de cette image de synthèse, pour l’une des 10 orientations discrètes utilisées lors de l’entrainement (incréments de 30 degrés). Afin de valider les performances de notre approche d’estimation d’orientation, nous avons construit une base de données de référence contenant 764 nuages de points. Ces données furent capturées à l’aide d’une caméra Kinect de Microsoft pour 30 volontaires différents, et la vérité-terrain sur l’orientation fut établie par l’entremise d’un système de capture de mouvement Vicon. Finalement, nous avons démontré les améliorations apportées par notre approche. En particulier, nous pouvons détecter des piétons avec une précision de 95.29% et estimer l’orientation du corps (dans un intervalle de 30 degrés) avec une précision de 88.88%. Nous espérons ainsi que nos résultats de recherche puissent servir de point de départ à d’autres recherches futures.The application of human behavior analysis has undergone rapid development during the last decades from entertainment system to professional one, as Human Robot Interaction (HRI), Advanced Driver Assistance System (ADAS), Pedestrian Protection System (PPS), etc. Meanwhile, this thesis addresses the problem of recognizing pedestrians and estimating their body orientation in 3D based on the fact that estimating a person’s orientation is beneficial in determining their behavior. In this thesis, a new method is proposed for detecting and estimating the orientation, in which the result of a pedestrian detection module and a orientation estimation module are integrated sequentially. For the goal of pedestrian detection, a cascade classifier is designed to draw a bounding box around the detected pedestrian. Following this, extracted regions are given to a discrete orientation classifier to estimate pedestrian body’s orientation. This classification is based on a coarse, rasterized depth image simulating a top-view virtual camera, and uses a support vector machine classifier that was trained to distinguish 10 orientations (30 degrees increments). In order to test the performance of our approach, a new benchmark database contains 764 sets of point cloud for body-orientation classification was captured. For this benchmark, a Kinect recorded the point cloud of 30 participants and a marker-based motion capture system (Vicon) provided the ground truth on their orientation. Finally we demonstrated the improvements brought by our system, as it detected pedestrian with an accuracy of 95:29% and estimated the body orientation with an accuracy of 88:88%.We hope it can provide a new foundation for future researches

New algorithms for the analysis of live-cell images acquired in phase contrast microscopy

La détection et la caractérisation automatisée des cellules constituent un enjeu important dans de nombreux domaines de recherche tels que la cicatrisation, le développement de l'embryon et des cellules souches, l’immunologie, l’oncologie, l'ingénierie tissulaire et la découverte de nouveaux médicaments. Étudier le comportement cellulaire in vitro par imagerie des cellules vivantes et par le criblage à haut débit implique des milliers d'images et de vastes quantités de données. Des outils d'analyse automatisés reposant sur la vision numérique et les méthodes non-intrusives telles que la microscopie à contraste de phase (PCM) sont nécessaires. Comme les images PCM sont difficiles à analyser en raison du halo lumineux entourant les cellules et de la difficulté à distinguer les cellules individuelles, le but de ce projet était de développer des algorithmes de traitement d'image PCM dans Matlab® afin d’en tirer de l’information reliée à la morphologie cellulaire de manière automatisée. Pour développer ces algorithmes, des séries d’images de myoblastes acquises en PCM ont été générées, en faisant croître les cellules dans un milieu avec sérum bovin (SSM) ou dans un milieu sans sérum (SFM) sur plusieurs passages. La surface recouverte par les cellules a été estimée en utilisant un filtre de plage de valeurs, un seuil et une taille minimale de coupe afin d'examiner la cinétique de croissance cellulaire. Les résultats ont montré que les cellules avaient des taux de croissance similaires pour les deux milieux de culture, mais que celui-ci diminue de façon linéaire avec le nombre de passages. La méthode de transformée par ondelette continue combinée à l’analyse d'image multivariée (UWT-MIA) a été élaborée afin d’estimer la distribution de caractéristiques morphologiques des cellules (axe majeur, axe mineur, orientation et rondeur). Une analyse multivariée réalisée sur l’ensemble de la base de données (environ 1 million d’images PCM) a montré d'une manière quantitative que les myoblastes cultivés dans le milieu SFM étaient plus allongés et plus petits que ceux cultivés dans le milieu SSM. Les algorithmes développés grâce à ce projet pourraient être utilisés sur d'autres phénotypes cellulaires pour des applications de criblage à haut débit et de contrôle de cultures cellulaires.Automated cell detection and characterization is important in many research fields such as wound healing, embryo development, immune system studies, cancer research, parasite spreading, tissue engineering, stem cell research and drug research and testing. Studying in vitro cellular behavior via live-cell imaging and high-throughput screening involves thousands of images and vast amounts of data, and automated analysis tools relying on machine vision methods and non-intrusive methods such as phase contrast microscopy (PCM) are a necessity. However, there are still some challenges to overcome, since PCM images are difficult to analyze because of the bright halo surrounding the cells and blurry cell-cell boundaries when they are touching. The goal of this project was to develop image processing algorithms to analyze PCM images in an automated fashion, capable of processing large datasets of images to extract information related to cellular viability and morphology. To develop these algorithms, a large dataset of myoblasts images acquired in live-cell imaging (in PCM) was created, growing the cells in either a serum-supplemented (SSM) or a serum-free (SFM) medium over several passages. As a result, algorithms capable of computing the cell-covered surface and cellular morphological features were programmed in Matlab®. The cell-covered surface was estimated using a range filter, a threshold and a minimum cut size in order to look at the cellular growth kinetics. Results showed that the cells were growing at similar paces for both media, but their growth rate was decreasing linearly with passage number. The undecimated wavelet transform multivariate image analysis (UWT-MIA) method was developed, and was used to estimate cellular morphological features distributions (major axis, minor axis, orientation and roundness distributions) on a very large PCM image dataset using the Gabor continuous wavelet transform. Multivariate data analysis performed on the whole database (around 1 million PCM images) showed in a quantitative manner that myoblasts grown in SFM were more elongated and smaller than cells grown in SSM. The algorithms developed through this project could be used in the future on other cellular phenotypes for high-throughput screening and cell culture control applications

CRITICAL STUDIES NOTES: ART, BECOMING AND PARTICIPATION #13

Critical Studies: Art, Becoming and Participation marks the launch of the Visual Arts and Critical Studies Group, recently formed within the scope of the Arnaldo Araújo Research Centre (CEAA). The book seeks to rehearse what may become, in the next future, a Brute Theory, whose territories of action remain under definition but whose company we wish to keep along future trajectories. As the singular is a collective, in this publication dispersion overrides the search for any type of unity. Brute Theory, as a pocket of resistance to current models of academic domination anchored in profitable, efficient and, as repeatedly stated, competitive research, proposes precarious dynamics, such as risk and driSATH - Secção Autónoma de Teoria e História da Escola Superior Artística do Portoinfo:eu-repo/semantics/publishedVersio