ARG based on arcs and segments to improve the symbol recognition by genetic algorithm

URL : http://www.buyans.com/pol/UploadedFile/147_4410.pdfInternational audienceIn this paper a genetic matching scheme is extended to take into account primitive arcs and complex description in the pattern recognition process. Classical ways only focus on segments and are sensitive to over segmentation effect. Our approach allows to improve the recognition by handling more accurate description and also to decrease processing time by limiting the number of vertices to match. Experimental studies using real data attest the robustness of our approach

Laser Noise Reduction in Air

Fluctuations of the white-light supercontinuum produced by ultrashort laser pulses in selfguided filaments (spatio-temporal solitons) in air are investigated. We demonstrate that correlations exist within the white-light supercontinuum, and that they can be used to significantly reduce the laser intensity noise by filtering the spectrum. More precisely, the fundamental wavelength is anticorrelated with the wings of the continuum, while conjugated wavelength pairs on both sides of the continuum are strongly correlated. Spectral filtering of the continuum reduces the laser intensity noise by 1.2 dB, showing that fluctuations are rejected to the edges of the spectrum.Comment: 8 page

Reconnaissance de symboles graphiques à partir d'une combinaison de descripteurs en intégrant leur comportement sur une base d'apprentissage

National audienceDans cet article nous proposons une nouvelle approche pour combiner des descripteurs de formes fondée sur l'étude comportementale de ceux-ci vis-à-vis d'un corpus d'apprentissage. Chaque descripteur est calculé sur plusieurs classes d'objets ou de symboles. Pour chaque échantillon et pour tous les descripteurs nous déterminons un profil type défini à partir d'un jeu d'apprentissage en prenant en compte les conflits pouvant exister entre descripteurs. Enfin, nous montrons que l'utilisation de la combinaison de descripteurs permet d'améliorer la classification sur des cas d'études réels

Estimating landmarks on 2D images of beetle mandibles

Studying links between phenotype/genotype and agricultural practices is one of the main topics in agronomy research. Phenotypes can be characterized by informations like age, sex of animals/plants and more and more often with the help of image analysis of their morphology. From now, getting good quality of images for numerous individuals is easy but that leads to design automatic procedures to replace manual exploration of such amount of images. Several bottlenecks have been identified to analyze automatically images. One of them is segmentation of selected area and/or shapes, and another well-known one is setting automatically morphometric landmarks. Landmarks are points on the object which can be used to identify or to classify the objects. It exists a lot of methods to experiment landmarks setting, depending on the image contents. This work has been initiated by using the article of Palaniswamy et al. "Automatic identification of landmarks in digital images"[6]. They proposed a method based on calculus of a probabilistic Hough transform coupling to a template matching algorithm. They applied their method to the Drosophilia wings. In our study, we have gotten a set of 291 beetles . For each one 2D images of 5 different parts of their anatomy have been taken: mandibles left and right, head, pronotum and elytra. The first part of the project was to test how the Palaniswamy’s method could be used to analyze them. We have implemented all the required algorithms to compute positions of mandibles landmarks and compared the obtained results to landmarks which have been manually set by biologists. We will see that even positions automatically obtained are not fully precised, if we used centroid size to characterize mandibles, the size computed from automatic landmarks is closed to this one computed from the manual ones. Future works will focus on definition of a semi-landmarks procedure which would add some features as the measure of the curve between two landmarks