Régions actives morphologiques (application à la vision par ordinateur)

Nous proposons deux méthodes rapides de propagation d'un front d'onde comme des alternatives à l'utilisation des ensembles de niveaux zéro pour l'évolution de courbes et des surfaces. Ces deux méthodes approximent le mouvement affine d'une courbe, c'est-à-dire la propagation de la courbe en fonction de la courbure locale et d'une constante, en traitant séparément la contribution de ces deux termes sans calculer explicitement la courbure locale. Les contours actifs morphologiques utilisent une région binaire pour faire évoluer une courbe à l'aide des opérations morphologiques basiques et des substitutions des configurations...This work presents two fast methods to simulate the motion of evolving surfaces with a normal velocity equal to mean curvature plus a constant (affine motion) as an alternative of the use of level set methods with usual finite difference discretization. These methods approximate a curvature-driven flow, processing separately normal constant motion and mean curvature motion, without calculate local curvature explicitly...PARIS5-BU Saints-Pères (751062109) / SudocSudocFranceF

A new experimental ground vehicle with hybrid control and hybrid vision sensor

ASERInternational audienc

A new experimental ground vehicle with hybrid control and hybrid vision sensor

ASERInternational audienceThis paper presents a new hybrid control algorithm based on saturation functions and its real–time application to a ground vehicle. The hybrid control is developed from a nonlinear continuous control law and the objective is to obtain the optimal sampling period T to apply the controller in real experiences. The stability analysis was made in discrete time. The experimental platform is composed of a remote control toy car and a vision system. The vision system is built using a simple webcam and a diode laser. This system is fast, accurate, inexpensive and easy to implement. Simulations and experiments show the stability and robustness of the closed–loop system. The proposed control law performance is compared with a linear control algorithm

A new experimental ground vehicle with hybrid control and hybrid vision sensor

ASERInternational audienceThis paper presents a new hybrid control algorithm based on saturation functions and its real–time application to a ground vehicle. The hybrid control is developed from a nonlinear continuous control law and the objective is to obtain the optimal sampling period T to apply the controller in real experiences. The stability analysis was made in discrete time. The experimental platform is composed of a remote control toy car and a vision system. The vision system is built using a simple webcam and a diode laser. This system is fast, accurate, inexpensive and easy to implement. Simulations and experiments show the stability and robustness of the closed–loop system. The proposed control law performance is compared with a linear control algorithm

A Curriculum Learning Approach to Classify Nitrogen Concentration in Greenhouse Basil Plants Using a Very Small Dataset and Low-Cost RGB Images

The automatic classification of plants with nutrient deficiencies or excesses is essential in precision agriculture. In particular, being able to perform early detection of nutrient concentrations would increase the production of crop yields and make appropriate use of fertilizers. RGB cameras represent a low-cost alternative sensor for plant monitoring, but this task is complicated when it is purely visual and has limited samples. In this paper, we analyze the Curriculum by Smoothing technique with a small dataset of RGB images (144 images per class) to classify nitrogen concentrations in greenhouse basil plants. This Deep Learning method changes the texture found in the images during training by convolving each feature map (the output of a convolutional layer) of a Convolutional Neural Network with a Gaussian kernel whose width increases as training progresses. We observed that controlled information extraction allows a state-of-the-art deep neural network to perform well using little training data containing a high variance between items of the same class. As a result, the Curriculum by Smoothing provides an average accuracy 7% higher than the traditional transfer learning method for the classification of the nitrogen concentration level of greenhouse basil ‘Nufar’ plants with little data

Extending resolution within a single imaging frame

The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.Fil: Torres García, Esley. Universidad Nacional Autónoma de México; MéxicoFil: Pinto Cámara, Raúl. Universidad Nacional Autónoma de México; MéxicoFil: Linares, Alejandro. Universidad Nacional Autónoma de México; MéxicoFil: Martínez, Damián. Universidad Nacional Autónoma de México; MéxicoFil: Abonza, Víctor. Universidad Nacional Autónoma de México; MéxicoFil: Brito Alarcón, Eduardo. Universidad Nacional Autónoma de México; MéxicoFil: Calcines Cruz, Carlos. Universidad Nacional Autónoma de México; MéxicoFil: Valdés Galindo, Gustavo. Universidad Nacional Autónoma de México; MéxicoFil: Torres, David. Universidad Nacional Autónoma de México; MéxicoFil: Jabloñski, Martina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Torres Martínez, Héctor H.. Universidad Nacional Autónoma de México; MéxicoFil: Martínez, José L.. Universidad Nacional Autónoma de México; MéxicoFil: Hernández, Haydee O.. Universidad Nacional Autónoma de México; MéxicoFil: Ocelotl Oviedo, José P.. Universidad Nacional Autónoma de México; MéxicoFil: Garcés, Yasel. Universidad Nacional Autónoma de México; MéxicoFil: Barchi, Marco. University of Rome Tor Vergata; ItaliaFil: D'Antuono, Rocco. Crick Advanced Light Microscopy Facility; Reino UnidoFil: Bošković, Ana. European Molecular Biology Laboratory; AlemaniaFil: Dubrovsky, Joseph G.. Universidad Nacional Autónoma de México; MéxicoFil: Darszon, Alberto. Universidad Nacional Autónoma de México; MéxicoFil: Buffone, Mariano Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Rodríguez Morales, Roberto. No especifíca;Fil: Rendon Mancha, Juan Manuel. Universidad Autónoma del Estado de Morelos; MéxicoFil: Wood, Christopher D.. Universidad Autónoma del Estado de Morelos; MéxicoFil: Hernández García, Armando. Universidad Autónoma del Estado de Morelos; MéxicoFil: Krapf, Diego. University of Colorado; Estados UnidosFil: Crevenna, Álvaro H.. European Molecular Biology Laboratory; ItaliaFil: Guerrero, Adán. Universidad Autónoma del Estado de Morelos; Méxic