Complex networks : application for texture characterization and classification

This article describes a new method and approach of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we present how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditional and extended hierarchical measurements, are used to characterize "organization" of textures

Analyse en ondelettes orthogonales pour la détection de défauts sur des produits manufacturés

Nous présentons une application de l'analyse multirésolution en ondelettes orthogonales au contrôle qualité par vision artificielle de filets de bouchons en polyéthylène. A partir du signal monodimensionnel, issu d'une caméra linéaire visant l'intérieur du bouchon, nous calculons les coefficients des trois premiers niveaux de résolution. Les densités d'énergie des coefficients, observés sur une zone contenant le filet, fournissent trois paramètres discriminants pour la distinction entre filet correct et filet défectueux et alimentent un classifieur non supervisé après une phase d'apprentissage

Etudes comparatives de différents détecteurs de contours et segmentation au sens contours par frames multiéchelles

Nous abordons l'étude par une comparaison de différents détecteurs de contours, basée sur les performances et la lourdeur d'implémentation de chacun. Puis, nous présentons un système de segmentation à partir de contours multiéchelles sur des images réelles. Nous nous basons sur une généralisation du filtre de Canny, optimisé pour un contour plus réaliste que le contour traditionnel sous forme d'échelon. Ce filtre peut être utilisé pour générer une famille d'ondelettes non orthogonales. Pour la fusion des données de projections nous utilisons un classifieur géométrique développé dans notre laboratoire

Complex networks : application for texture characterization and classification

This article describes a new method and approach of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we present how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditional and extended hierarchical measurements, are used to characterize "organization" of textures

Inferring information across scales in acquired complex signals

Abstract. Transmission of information across the scales of a complex signal has some interesting potential, notably in the derivation of subpixel information, cross-scale inference and data fusion. It follows the structure of complex signals themselves, when they are considered as acquisitions of complex systems. In this work we contemplate the problem of cross-scale information inference through the determination of appropriate multiscale decomposition. Our goal is to derive a generic methodology that can be applied to propagate information across the scales in a wide variety of complex signals. Consequently, we first focus on the determination of appropriate multiscale characteristics, and we show that singularity exponents computed in microcanonical formulations are much better candidates for the characterization of transitions in complex signals: they outperform the classical «linear filtering » approach of the state-of-the-art edge detectors (for the case of 2D signals). This is a fundamental topic as edges are usually considered as important multiscale features in an image. The comparison is done within the formalism of reconstructible systems. Critical exponents, naturally associated to phase transitions and used in complex systems methods in the framework of criticality are key notions in Statistical Physics that can lead to the complete determination of the geometrical cascade properties in complex signals. We study optimal multiresolution analysis associated to critical exponents through the concept of «optimal wavelet». We demonstrate the usefulness of multiresolution analysis associated to critical exponents in two decisive examples: the reconstruction of perturbated optical phase in Adaptive Optics (AO) and the generation of high resolution ocean dynamics from low resolution altimetry data.