Adaptative Tracking of Non Rigid Objects Based on Color Histograms and Automatic Parameter Selection

International audienceOne of the main difficulties in visual tracking is to take into account appearance changes (not only of the target but also of or due to the scene, illumination for example). The use of a Bayesian framework is very flexible and has proven to be very efficient in visual tracking. Moreover, color or greylevel histograms allow to track an objet with a low computational cost. The recently proposed color-based trackers integrated in a probabilistic framework are efficient for a given application (face tracking for example) but can not be generalized easily, due to the initialization and the adjustment of the different tracker parameters that are dependent on the input sequence. This paper presents a method based on color integrated in a particle filter that allows to cope with some of the usual problems of visual tracking (occlusions, target appearance changes, changes in resolution or in illumination) and to adapt easily to different applications (tracking of structures in aerial imagery as well as football players). The novelty of the tracker is its ability to automatically regulate all the parameters needed for tracking, which makes it flexible and easily usable for different applications

Advantages of 18F-FDG PET/CT imaging over modified Duke criteria and clinical presumption in patients with challenging suspicion of infective endocarditis

According to European Society of Cardiology guidelines (ESC2015) for infective endocarditis (IE) management, modified Duke criteria (mDC) are implemented with a degree of clinical suspicion degree, leading to grades such as possible or rejected IE despite a persisting high level of clinical suspicion. Herein, we evaluate th

Multilingual RECIST classification of radiology reports using supervised learning.

OBJECTIVES The objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages. METHODS In our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation. RESULTS The best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks. CONCLUSIONS These results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers

Laugh machine

The Laugh Machine project aims at endowing virtual agents with the capability to laugh naturally, at the right moment and with the correct intensity, when interacting with human participants. In this report we present the technical development and evaluation of such an agent in one specific scenario: watching TV along with a participant. The agent must be able to react to both, the video and the participant’s behaviour. A full processing chain has been implemented, inte- grating components to sense the human behaviours, decide when and how to laugh and, finally, synthesize audiovisual laughter animations. The system was evaluated in its capability to enhance the affective experience of naive participants, with the help of pre and post-experiment questionnaires. Three interaction conditions have been compared: laughter-enabled or not, reacting to the participant’s behaviour or not. Preliminary results (the number of experiments is currently to small to obtain statistically significant differences) show that the interactive, laughter-enabled agent is positively perceived and is increasing the emotional dimension of the experiment

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Estimation du nombre de sommets avec la méthode du Contour Actif Statistique Polygonal

Nous proposons d'adapter une technique basée sur le principe de description de longueur minimale (MDL) afin d'estimer la complexité - proportionnelle au nombre de sommets - du polygone employé pour effectuer la segmentation avec des Contours Actifs Statistiques Polygonaux. Nous montrons qu'il est possible d'estimer efficacement ce nombre de sommets, à condition qu'une stratégie d'optimisation en deux étapes soit mise en oeuvre. Nous obtenons ainsi une méthode de segmentation rapide reposant sur l'optimisation d'un critère sans paramètre libre

Reconnaissance des formes par Contour Actif Statistique - Application à l'imagerie optronique active

Les systèmes d'imagerie actifs permettent d'acquérir des images de jour comme de nuit, avec une résolution supérieure à celle des équipements infra-rouges. Cependant, l'inconvénient de ces systèmes par rapport aux capteurs optiques classiques est qu'ils délivrent des images fortement dégradées par le phénomène de speckle, qui en limite l'interpretation automatique. Nous proposons, au cours de cette thèse, d'étudier dans quelle mesure la technique du Contour Actif Statistique Polygonal (CASP) peut etre employée afin d'effectuer la reconnaissance des objets présents dans des images de speckle. La méthode de reconnaissance des formes employée correspond à l'algorithme du plus proche voisin; on selectionne ainsi la référence la plus proche de la silhouette obtenue avec le CASP, en évaluant une certaine mesure de comparaison entre contours.AIX-MARSEILLE3-BU Sc.St Jérô (130552102) / SudocSudocFranceF