Color display for multiwavelength astronomical images

This paper proposes a new approach for the color display of multispectral/hyperspectral images. The color representation of such data becomes problematic when the number of bands is higher than three, i.e. the basic RGB (Red, Green, Blue) representation is not straightforward. Here we employ a technique that uses a segmentation map, like an a priori information, and then compute a Factorial Discriminant Analysis (Fischer analysis) in order to allow, at best, a distribution of the information in the color space HSV (Hue, Saturation, Value). The information collected from the segmentation map (where each pixel is associated with class) has been shown to be advantages in the representation of the images through the results obtained on increasing size image collections in the framework of astronomical images. This method can easily be applied to other domains such as polarimetric or remote sensing imagery.Cet article propose une nouvelle méthode de représentation et de visualisation en couleur d'images multispectrales ou hyperspectrales. Le problème de la visualisation de telles données est en effet problématique dès que le nombre de bandes spectrales est supérieur à trois, i.e., la représentation triviale RVB (Rouge, Vert, Bleu) n'est plus directe. Le principe consiste ici à utiliser une carte de segmentation préalablement obtenue, a priori, et à réaliser une analyse factorielle discriminante permettant de distribuer au mieux l'information dans l'espace des couleurs TSL (Teinte, Saturation, Luminance). L'information apportée par la carte de segmentation (chaque site est associé à une classe) peut se révéler judicieuse comme le montrent les résultats obtenus sur des lots d'images de tailles croissantes dans le cadre de l'imagerie astronomique. Cette méthode est générale et s'applique également à d'autres domaines manipulant des images multicomposantes ou multivariées comme en télédétection ou en imagerie polarimétrique

Face detection using classifiers cascade based on vector angle measure and multi-modal representation

This paper deals with face detection in still gray level images which is the first step in many automatic systems like video surveillance, face recognition, and images data base management. We propose a new face detection method using a classifiers cascade, each of which is based on a vector angle similarity measure between the investigated window and the face and nonface representatives (centroids). The latter are obtained using a clustering algorithm based on the same measure within the current training data sets, namely the low confidence classified samples at the previous stage of the cascade. First experiment results on refereed face data test sets are very satisfactory

Markovian regularization of latent-variable-models mixture for New multi-component image reduction/segmentation scheme

This paper proposes a new framework for multi-component images segmentation which plays an increasing role in many imagery applications like astronomy, medicine, remote sensing, chemistry, biology etc. In fact, inference on such images is a very difficult task when the number of components increases due to the well-known Hughes phenomenon. A common solution is to reduce dimensionality, keeping only relevant information before segmentation. Linear models usually fail with complex data structure, and mixture of linear models, each of which modeling a local cluster of the data, is more suitable. Moreover, a probabilistic formulation based on linear latent variable models allows efficient solution using a maximum-likelihood-based decision to recover the clusters. However, for multi-component image classification, this is not enough because it completely neglects the spatial positions of the multi-dimensional pixels on the lattice. Therefore, we propose to consider the neighborhood by introducing a Markovian a priori to efficiently regularize pixel classification. As a consequence, segmentation and reduction are performed simultaneously in an efficient and robust way. In this paper, we focus on the Probabilistic Principal Component Analysis (PPCA) as a latent variable model, and the Hidden Markov quad-Tree (HMT) as an a priori for regularization. The method performs well both on synthetic and real remote sensing and Stokes-Mueller images. © 2007 Springer-Verlag London Limited

Drift invariant gas recognition technique for on chip tin oxide gas sensor array

The purpose of this paper is the study of the robustness of a new low complexity recognition method based on the measurement issued from an on chip 4 x 4 Tin oxide gas sensor array. The recognition system is based on a vector angle similarity measure between the query gas and the representatives of the different gas classes. The latter are obtained using a clustering algorithm based on the same measure within the training data set. Experimented results show more than 98% of good recognition and the robustness or the proposed approach is tested by recognizing gas measurements with simulated drift. Less than 1% of performance degradation is noted at the worst case

Temperature modulation for tin-oxide gas sensors

This paper presents a study of temperature modulation for tin-oxide gas sensor. The main objective of this work is to per-form temperature modulation experimental setup for tin oxide gas sensors in order to improve the selectivity of the sensor array and to virtually increase the number of sensors. Typically, tin oxide sensors operate by heating at a relatively high temperature (around 300 degrees C a microhotplate structure). A convex microhotplate is proposed in order to improve the thermal properties of the structure and enable efficient temperature modulation process to be carried-out. Temperature modulation is shown to increase the number of our sensors from 16 physical sensors (integrated on-chip) up to 12 000 virtual sensors. This will enable the emulation of a very large number of sensors typically found in biological systems

Probabilistic satellite image fusion

Remote sensing satellite images play an important role in many applications such as environment and agriculture lands monitoring. In such images the scene is usually observed with different modalities, e.g. wavelengths. Image Fusion is an important analysis tool that summarizes the available information in a unique composite image. This paper proposes a new transform domain image fusion (IF) algorithm based on a hierarchical vector hidden Markov model (HHMM) and the mixture of probabilistic principal component analysers. Results on real Landsat images, quantified subjectively and using objective measures, are very satisfactory. © 2009 Springer Berlin Heidelberg

Role of the excited electronic states in the ionization of ambient air by a nanosecond discharge

International audienceIn this paper, the mechanism of air ionization by a single nanosecond discharge under atmospheric conditions is studied by numerical simulations. The plasma kinetics is solved with ZDPlasKin and the electron energy distribution function is calculated with BOLSIG+. The originality of the model is to consider not only the excited electronic states of N2, but also the excited electronic states of O and N. These states are shown to have a primary importance in the ionization of the plasma for ne > 10 17 cm-3. It is shown that a non-equilibrium plasma (Te > Tgas) at ne = 10 17 cm-3 can reach full ionization and thermalization (Te = Tgas ≈ 3 eV, ne ≈ 10 19 cm-3) in less than half a nanosecond under a field usually encountered in nanosecond discharges