Morphology of the very inclined debris disk around HD 32297

Direct imaging of circumstellar disks at high angular resolution is mandatory to provide morphological information that bring constraints on their properties, in particular the spatial distribution of dust. New techniques combining observing strategy and data processing now allow very high contrast imaging with 8-m class ground-based telescopes (10^-4 to 10^-5 at ~1") and complement space telescopes while improving angular resolution at near infrared wavelengths. We carried out a program at the VLT with NACO to image known debris disks with higher angular resolution in the near IR than ever before in order to study morphological properties and ultimately to detect signpost of planets. The observing method makes use of advanced techniques: Adaptive Optics, Coronagraphy and Differential Imaging, a combination designed to directly image exoplanets with the upcoming generation of "planet finders" like GPI (Gemini Planet Imager) and SPHERE (Spectro-Polarimetric High contrast Exoplanet REsearch). Applied to extended objects like circumstellar disks, the method is still successful but produces significant biases in terms of photometry and morphology. We developed a new model-matching procedure to correct for these biases and hence to bring constraints on the morphology of debris disks. From our program, we present new images of the disk around the star HD 32297 obtained in the H (1.6mic) and Ks (2.2mic) bands with an unprecedented angular resolution (~65 mas). The images show an inclined thin disk detected at separations larger than 0.5-0.6". The modeling stage confirms a very high inclination (i=88{\deg}) and the presence of an inner cavity inside r_0~110AU. We also found that the spine (line of maximum intensity along the midplane) of the disk is curved and we attributed this feature to a large anisotropic scattering factor (g~0.5, valid for an non-edge on disk). Abridged ...Comment: 12 pages, 10 figures, accepted for publication in Astronomy and Astrophysic

Bayesian multi-modal model comparison: a case study on the generators of the spike and the wave in generalized spike–wave complexes

We present a novel approach to assess the networks involved in the generation of spontaneous pathological brain activity based on multi-modal imaging data. We propose to use probabilistic fMRI-constrained EEG source reconstruction as a complement to EEG-correlated fMRI analysis to disambiguate between networks that co-occur at the fMRI time resolution. The method is based on Bayesian model comparison, where the different models correspond to different combinations of fMRI-activated (or deactivated) cortical clusters. By computing the model evidence (or marginal likelihood) of each and every candidate source space partition, we can infer the most probable set of fMRI regions that has generated a given EEG scalp data window. We illustrate the method using EEG-correlated fMRI data acquired in a patient with ictal generalized spike–wave (GSW) discharges, to examine whether different networks are involved in the generation of the spike and the wave components, respectively. To this effect, we compared a family of 128 EEG source models, based on the combinations of seven regions haemodynamically involved (deactivated) during a prolonged ictal GSW discharge, namely: bilateral precuneus, bilateral medial frontal gyrus, bilateral middle temporal gyrus, and right cuneus. Bayesian model comparison has revealed the most likely model associated with the spike component to consist of a prefrontal region and bilateral temporal–parietal regions and the most likely model associated with the wave component to comprise the same temporal–parietal regions only. The result supports the hypothesis of different neurophysiological mechanisms underlying the generation of the spike versus wave components of GSW discharges

Autonomous monitoring of cliff nesting seabirds using computer vision

In this paper we describe a proposed system for automatic visual monitoring of seabird populations. Image sequences of cliff face nesting sites are captured using time-lapse digital photography. We are developing image processing software which is designed to automatically interpret these images, determine the number of birds present, and monitor activity. We focus primarily on the the development of low-level image processing techniques to support this goal. We first describe our existing work in video processing, and show how it is suitable for this problem domain. Image samples from a particular nest site are presented, and used to describe the associated challenges. We conclude by showing how we intend to develop our work to construct a distributed system capable of simultaneously monitoring a number of sites in the same locality

Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction

Recently developed deep neural networks achieved state-of-the-art results in the subject of 6D object pose estimation for robot manipulation. However, those supervised deep learning methods require expensive annotated training data. Current methods for reducing those costs frequently use synthetic data from simulations, but rely on expert knowledge and suffer from the "domain gap" when shifting to the real world. Here, we present a proof of concept for a novel approach of autonomously generating annotated training data for 6D object pose estimation. This approach is designed for learning new objects in operational environments while requiring little interaction and no expertise on the part of the user. We evaluate our autonomous data generation approach in two grasping experiments, where we archive a similar grasping success rate as related work on a non autonomously generated data set.Comment: This is a preprint and currently under peer review at IROS 202

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Hierarchical improvement of foreground segmentation masks in background subtraction

A plethora of algorithms have been defined for foreground segmentation, a fundamental stage for many computer vision applications. In this work, we propose a post-processing framework to improve foreground segmentation performance of background subtraction algorithms. We define a hierarchical framework for extending segmented foreground pixels to undetected foreground object areas and for removing erroneously segmented foreground. Firstly, we create a motion-aware hierarchical image segmentation of each frame that prevents merging foreground and background image regions. Then, we estimate the quality of the foreground mask through the fitness of the binary regions in the mask and the hierarchy of segmented regions. Finally, the improved foreground mask is obtained as an optimal labeling by jointly exploiting foreground quality and spatial color relations in a pixel-wise fully-connected Conditional Random Field. Experiments are conducted over four large and heterogeneous datasets with varied challenges (CDNET2014, LASIESTA, SABS and BMC) demonstrating the capability of the proposed framework to improve background subtraction resultsThis work was partially supported by the Spanish Government (HAVideo, TEC2014-53176-R

Search for cool giant exoplanets around young and nearby stars - VLT/NaCo near-infrared phase-coronagraphic and differential imaging

[Abridged] Context. Spectral differential imaging (SDI) is part of the observing strategy of current and future high-contrast imaging instruments. It aims to reduce the stellar speckles that prevent the detection of cool planets by using in/out methane-band images. It attenuates the signature of off-axis companions to the star, such as angular differential imaging (ADI). However, this attenuation depends on the spectral properties of the low-mass companions we are searching for. The implications of this particularity on estimating the detection limits have been poorly explored so far. Aims. We perform an imaging survey to search for cool (Teff<1000-1300 K) giant planets at separations as close as 5-10 AU. We also aim to assess the sensitivity limits in SDI data taking the photometric bias into account. This will lead to a better view of the SDI performance. Methods. We observed a selected sample of 16 stars (age < 200 Myr, d < 25 pc) with the phase-mask coronagraph, SDI, and ADI modes of VLT/NaCo. Results. We do not detect any companions. As for the sensitivity limits, we argue that the SDI residual noise cannot be converted into mass limits because it represents a differential flux, unlike the case of single-band images. This results in degeneracies for the mass limits, which may be removed with the use of single-band constraints. We instead employ a method of directly determining the mass limits. The survey is sensitive to cool giant planets beyond 10 AU for 65% and 30 AU for 100% of the sample. Conclusions. For close-in separations, the optimal regime for SDI corresponds to SDI flux ratios >2. According to the BT-Settl model, this translates into Teff<800 K. The methods described here can be applied to the data interpretation of SPHERE. We expect better performance with the dual-band imager IRDIS, thanks to more suitable filter characteristics and better image quality.Comment: 19 pages, 16 figures, accepted for publication in A&A, version including language editin

Bayesian methods of astronomical source extraction

We present two new source extraction methods, based on Bayesian model selection and using the Bayesian Information Criterion (BIC). The first is a source detection filter, able to simultaneously detect point sources and estimate the image background. The second is an advanced photometry technique, which measures the flux, position (to sub-pixel accuracy), local background and point spread function. We apply the source detection filter to simulated Herschel-SPIRE data and show the filter's ability to both detect point sources and also simultaneously estimate the image background. We use the photometry method to analyse a simple simulated image containing a source of unknown flux, position and point spread function; we not only accurately measure these parameters, but also determine their uncertainties (using Markov-Chain Monte Carlo sampling). The method also characterises the nature of the source (distinguishing between a point source and extended source). We demonstrate the effect of including additional prior knowledge. Prior knowledge of the point spread function increase the precision of the flux measurement, while prior knowledge of the background has onlya small impact. In the presence of higher noise levels, we show that prior positional knowledge (such as might arise from a strong detection in another waveband) allows us to accurately measure the source flux even when the source is too faint to be detected directly. These methods are incorporated in SUSSEXtractor, the source extraction pipeline for the forthcoming Akari FIS far-infrared all-sky survey. They are also implemented in a stand-alone, beta-version public tool that can be obtained at http://astronomy.sussex.ac.uk/$\sim$rss23/sourceMiner\_v0.1.2.0.tar.gzComment: Accepted for publication by ApJ (this version compiled used emulateapj.cls