Discriminant analysis of solar bright points and faculae I. Classification method and center-to-limb distribution

While photospheric magnetic elements appear mainly as Bright Points (BPs) at the disk center and as faculae near the limb, high-resolution images reveal the coexistence of BPs and faculae over a range of heliocentric angles. This is not explained by a "hot wall" effect through vertical flux tubes, and suggests that the transition from BPs to faculae needs to be quantitatively investigated. To achieve this, we made the first recorded attempt to discriminate BPs and faculae, using a statistical classification approach based on Linear Discriminant Analysis(LDA). This paper gives a detailed description of our method, and shows its application on high-resolution images of active regions to retrieve a center-to-limb distribution of BPs and faculae. Bright "magnetic" features were detected at various disk positions by a segmentation algorithm using simultaneous G-band and continuum information. By using a selected sample of those features to represent BPs and faculae, suitable photometric parameters were identified in order to carry out LDA. We thus obtained a Center-to-Limb Variation (CLV) of the relative number of BPs and faculae, revealing the predominance of faculae at all disk positions except close to disk center (mu > 0.9). Although the present dataset suffers from limited statistics, our results are consistent with other observations of BPs and faculae at various disk positions. The retrieved CLV indicates that at high resolution, faculae are an essential constituent of active regions all across the solar disk. We speculate that the faculae near disk center as well as the BPs away from disk center are associated with inclined fields

Model-image registration of a building’s facade based on dense semantic segmentation

International audienceThis article presents an efficient approach for accurate registration of a building facade model "dressed" with dense semantic information. Localization sensors such as the GPS as well as vision-based methods are able to provide a camera pose in an efficient and stable way, but at the expense of low accuracy. We propose here to rely on semantic maps to improve the accuracy of a rough camera pose. Simultaneously we aim to iteratively improve the quality of the semantic map through the registration. Registration and semantic segmentation are jointly refined in an Expectation-Maximization framework. We especially introduce a Bayesian model that uses prior semantic segmentation as well as geometric structure of the facade reference modeled by Generalized Gaussian Mixtures. We show the advantages of our method in terms of robustness to clutter and change of illumination on urban images from various databases

Facade Proposals for Urban Augmented Reality

International audienceWe introduce a novel object proposals method specific to building facades. We define new image cues that measure typical facadecharacteristics such as semantic, symmetry and repetitions. They are combined to generate a few facade candidates in urban environments fast. We show that our method outperforms state-of-the-art object proposals techniques for this task on the 1000 images of the Zurich Building Database. We demonstrate the interest of this procedure for augmented reality through facade recognition and camera pose initialization. In a very time-efficient pipeline we classify the candidates and match them to a facade references database using CNN-based descriptors. We prove that this approach is more robust to severe changes of viewpoint and occlusions than standard object recognition methods

Génération d'hypothèses de façades utilisant des critères contextuels et structurels

National audienceIn that article we focus on facade detection in order to improve image/model buildings matching for pose computation in urbain environnement. We use a two-step design. First a cascade of LogitBoost classifiers using features which describe local context selects a few windows from a set of windows drawn on an a priori distribution. These facade candidates are then more internally described using their Haar-Fourier representation. Eventually they are discarded or kept by a strong classifier SVM. Results are computed from a 410-set of urban images.Dans cet article nous nous intéressons à la détection de façades dans le but d'améliorer la mise en correspon-dance image/modèle de bâtiments pour le calcul de pose en milieux urbain. Après une étape de rectification automa-tique, nous employons un schéma en deux étapes. Premiè-rement une cascade de classifieurs LogitBoost basés sur des indices simples faisant intervenir le contexte local sé-lectionne quelques fenêtres parmi un ensemble de fenêtres tirées selon une distribution a priori. Ces façades poten-tielles sont ensuite décrites plus structurellement par leur représentation de Haar-Fourier. Elles sont finalement rete-nues ou écartées par un classifieur fort SVM. Les résultats sont évalués sur une base de test de 410 images urbaines

Prior-based facade rectification for AR in urban environment

International audienceWe present a method for automatic facade rectification and detection in the Manhattan world scenario. A Bayesian inference approach is proposed to recover the Manhattan directions in camera coordinate system, based on a prior we derived from the analysis of urban datasets. In addition, a SVM-based procedure is used to identify right-angle corners in the rectified images. These corners are clustered in facade regions using a greedy rectangular min-cut technique. Experiments on a standard dataset show that our algorithm performs better or as well as state-of-the-art techniques while being much faster

An Exploratory Pilot Study

While mental health treatments have proven to be effective for a range of mental health problems, there is comparably little research on its effects on personality disorders or difficulty (PD). New dimensional conceptualizations of PD such as the ICD-11 PD model enable the cost- and time-effective dimensional assessment of severity and style of PD. Furthermore, they constitute a promising tool to investigate PD, not only as a treatment endpoint but also as a predictive or influencing factor for mental health treatments. In this study, we investigated the effects in two different mental health treatment settings [online (N = 38); face-to-face and blended [FTF/blended] (N = 35)] on the reduction of maladaptive personality traits as well as the interaction between maladaptive personality patterns and the response on primary endpoints (i.e., mental distress). Results indicate that both treatment settings have comparable within-group effects on the reduction of distress symptoms, while the treatment in the FTF/blended setting seems to have a stronger impact on the reduction of maladaptive traits. Further, reduction of maladaptive trait expressions was a reliable predictor of treatment response in the FTF/blended setting while explaining less variance in the online setting. Beyond the promising findings on the utility of maladaptive trait change as an outcome measure, we discuss possible applications as an information source for treatment decisions

A Simple and Effective Method to Detect Orthogonal Vanishing Points in Uncalibrated Images of Man-Made Environments

International audienceThis paper presents an effective and easy-to-implement algorithm to compute orthogonal vanishing points in uncalibrated images of man-made scenes. The main contribution is to estimate the zenith and the horizon line before detecting the vanishing points, using simple properties of the central projection and exploiting accumulations of oriented segments around the horizon. Our method is fast and yields an accuracy comparable, and even better in some cases, to that of state-of-the-art algorithms