Intégration de la saillance visuelle dans la reconnaissance d’évènements rares

This paper presents a new method for the detection of rares events in video. It is based on the visual saliency and on the detection and local description of points of interest. The point-of-interest filtering is carried out using the saliency score, allowing only those with visual importance to be considered. A model of normal events is learned thanks to the probabilistic generative model "Latent Dirichlet Allocation" (LDA), known for its performance in textual data mining. The detection of an abnormal or rare event is carried out in a probabilistic way via the learned model. This paper proposes to combine a saliency based visual focalization and the use of automatic document classification technic in order to classify images from a video and to detect rare events

A very simple framework for 3D human poses estimation using a single 2D image: Comparison of geometric moments descriptors.

In this paper, we propose a framework in order to automatically extract the 3D pose of an individual from a single silhouette image obtained with a classical low-cost camera without any depth information. By pose, we mean the configuration of human bones in order to reconstruct a 3D skeleton representing the 3D posture of the detected human. Our approach combines prior learned correspondences between silhouettes and skeletons extracted from simulated 3D human models publicly available on the internet. The main advantages of such approach are that silhouettes can be very easily extracted from video, and 3D human models can be animated using motion capture data in order to quickly build any movement training data. In order to match detected silhouettes with simulated silhouettes, we compared geometrics invariants moments. According to our results, we show that the proposed method provides very promising results with a very low time processing

3D Human Poses Estimation from a Single 2D Silhouette

This work focuses on the problem of automatically extracting human 3D poses from a single 2D image. By pose we mean the configuration of human bones in order to reconstruct a 3D skeleton representing the 3D posture of the detected human. This problem is highly non-linear in nature and confounds standard regression techniques. Our approach combines prior learned correspondences between silhouettes and skeletons extracted from 3D human models. In order to match detected silhouettes with simulated silhouettes, we used Krawtchouk geometric moment as shape descriptor. We provide quantitative results for image retrieval across different action and subjects, captured from differing viewpoints. We show that our approach gives promising result for 3D pose extraction from a single silhouette

Favorable and unfavorable conditions for the development of the self determination of students in the process of cognitive activity

У статті представлено результати емпіричного дослідження самодетермінації учнів в процесі пізнавальної діяльності. Описуються діагностичні методики. Факторний аналіз засвідчує, що існують сприятливі і несприятливі умови розвитку самодетермінації. Доводиться позитивний вплив особистісної самодетермінації на розвиток пізнавальної діяльності.Results of empirical research of the self-determination of students in the process of cognitive activity are presented in article. A description of diagnostic techniques is given also. Using factor analysis favorable and unfavorable conditions for the development of the self-determination were identified. It is also proved that the self-determination is a powerful catalyst for the development of cognitive activity. Blockade of the self-determination, respectively, inhibits the formation of cognitive activity

Virtual restoration of the Ghent altarpiece using crack detection and inpainting

In this paper, we present a new method for virtual restoration of digitized paintings, with the special focus on the Ghent Altarpiece (1432), one of Belgium's greatest masterpieces. The goal of the work is to remove cracks from the digitized painting thereby approximating how the painting looked like before ageing for nearly 600 years and aiding art historical and palaeographical analysis. For crack detection, we employ a multiscale morphological approach, which can cope with greatly varying thickness of the cracks as well as with their varying intensities (from dark to the light ones). Due to the content of the painting (with extremely many fine details) and complex type of cracks (including inconsistent whitish clouds around them), the available inpainting methods do not provide satisfactory results on many parts of the painting. We show that patch-based methods outperform pixel-based ones, but leaving still much room for improvements in this application. We propose a new method for candidate patch selection, which can be combined with different patch-based inpainting methods to improve their performance in crack removal. The results demonstrate improved performance, with less artefacts and better preserved fine details

High performance WR-1.5 corrugated horn based on stacked rings

We present the development and characterisation of a high frequency (500-750 GHz) corrugated horn based on stacked rings. A previous horn design, based on a Winston profile, has been adapted for the purpose of this manufacturing process without noticeable RF degradation. A subset of experimental results obtained using a vector network analyser are presented and compared to the predicted performance. These first results demonstrate that this technology is suitable for most commercial applications and also astronomical receivers in need of horn arrays at high frequencies.Comment: 9 page

Synthesis and conformational properties of 3,4-difluoro-L-prolines

Fluorinated proline derivatives have found diverse applications in areas ranging from medicinal chemistry over structural biochemistry to organocatalysis. Depending on the stereochemistry of monofluorination at the proline 3- or 4-position, different effects on the conformational properties of proline (ring pucker, cis/trans isomerization) are introduced. With fluorination at both 3- and 4-positions, matching or mismatching effects can occur depending on the relative stereochemistry. Here we report, in full, the syntheses and conformational properties of three out of the four possible 3,4-difluoro-L-proline diastereoisomers. The yet unreported conformational properties are described for (3S,4S)- and (3R,4R)-difluoro-L-proline, which are shown to bias ring pucker and cis/trans ratios on the same order of magnitude as their respective monofluorinated progenitors, although with significantly faster amide cis/trans isomerization rates. The reported analogues thus expand the scope of available fluorinated proline analogues as tools to tailor proline's distinct conformational and dynamical properties, allowing for the interrogation of its role in, for instance, protein stability or folding

Rare Events Detection and Localization In Crowded Scenes Based On Flow Signature

We introduce in this paper a novel method for rare events detection based on the optical flow signature. It aims to automatically highlight regions in videos where rare events are occurring. This kind of method can be used as an important step for many applications such as Closed-Circuit Television (CCTV) monitoring systems in order to reduce the cognitive effort of the operators by focusing their attention on the interesting regions. The proposed method exploits the properties of the Discrete Cosine Transform (DCT) applied to the magnitude and orientation maps of the optical flow. The output of the algorithm is a map where each pixel has a saliency score that indicates the presence of irregular motion regard to the scene. Based on the one class Support Vectors Machine (SVM) algorithm, a model of the frequent events is created and the rare events detection can be performed by using this model. The DCT is faster, easy to compute and gives interesting information to detect spatial irregular patterns in images [1]. Our method does not rely on any prior information of the scene and uses the saliency score as a feature descriptor. We demonstrate the potential of the proposed method on the publicly available videos dataset UCSD and show that it is competitive and outperforms some the state-of-the-art methods

Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes

In this paper, we propose a new method for rare event detection in crowded scenes using a combination of Color Histogram of Oriented Phases (CHOP) and Histogram of Oriented Optical Flow (HOOF). We propose to detect and filter spatio-temporal interest points (STIP) based on the visual saliency information of the scene. Once salient STIPs are detected, the motion and appearance information of the surrounding scene are extracted. Finally, the extracted information from normal scenes are modelled by using a Bayesian generative model (Latent Dirichlet Allocation). The rare events are detected by processing the likelihood of the current scene in regard to the obtained model. The proposed method has been tested on the publicly available UMN dataset and compared with different state-of-the-art algorithms. We have shown that our method is very competitive and provides promising results

Détection d'événements rares par modélisation de la signature du flot optique

International audienceNous présentons dans cet article une nouvelle méthode de détection d'événements rares basée sur l'analyse des mouvements saillants dans une scène. La méthode vise à localiser automatiquement toutes les régions d'une scène où se déroulent des événements rares. Un événement rare s'oppose aux événements fréquents en ce sens qu'on y intégre tout événement nouveau dans la scène. Elle exploite les propriétés de la transformée en cosinus discrète (TCD) qui, appliquée à une image, permet d'y détecter des irrégularités spatiales. En appliquant successivement la transformée et son inverse aux données de magnitude et d'orientation du flot optique, notre méthode permet de localiser tous les mouvements irréguliers. Une modélisation de ces mouvements, grâce à la version "one class" de l'algorithme SVM, permet d'identifier sur les événements rares parmi ceux fréquents. La méthode a été testée sur la base publique UCSD [1] et présente des résultats satisfaisants. Abstract-We present, in this paper, a new method for rare events détection based on salient motion analysis. The method aims to automatically locate all regions of a scene where rare events occur. A rare event is an event that not occurs frequently in the scene. It exploits properties of the discrete cosine transform (DCT) that can detect spatial irregularities in images. By performing the DCT and its inverse on the magnitude and orientation of the optical flow, our method makes it possible to locate any salient motion. Modeling of the salient motions thanks to the "one class" version of the support vector machine (SVM) allow the identification of rare events between salient motion. The method has been tested on the UCSD dataset and shows that it's promising