Fusion method choice driven by user constraints in a human gesture recognition context

International audienceRecognition of human gesture leads to a dynamic field that produces many various methods. The main way to improve the recognition process is to perform data fusion based on a qualification of each recognition method. The advance in data fusion gives also several solutions and the choice of a fusion method is a crucial point. The goal of this paper is to present an approach were the choice of the fusion method is driven by the constraints of the final user of data. Such constraints are expressed as bounds on indicators like confidence or accuracy. A practical experimentation illustrating this approach is presented in this paper. From a depth camera sensor, human gesture is interpreted and, fusion method is selected in accordance with the constraints of the final information consumer. This approach is illustrated in a robotic control by human gesture: outputs perform the action to be done by a NAO robot

Reconnaissance de feuilles d'arbres par fusion de décisions partielles

National audienceDans le cadre du développement d'une application Smartphone destinée à la reconnaissance des espèces d'arbres, une stratégie basée sur des sous-classifieurs a été mise en place pour reconnaître les feuilles à partir des caractéristiques liées à la base, au sommet et au contour. La théorie des fonctions de croyance est appliquée sur la sortie de chaque sous-classifieur afin de raffiner les résultats en diminuant l'effet de l'incertitude qui existe sur les caractéristiques des feuilles. La décision finale sur l'espèce de feuille est prise en transformant la croyance en probabilité pignistique et en accumulant les probabilités issues de chaque sous-classifieur pour chaque espèce. Les résultats démontrent que notre méthode de sous-classification et de décision obtient de bonnes performances

Curvature-Scale-based Contour Understanding for Leaf Margin Shape Recognition and Species Identification

International audienceIn the frame of a tree species identifying mobile application, designed for a wide scope of users, and with didactic purposes, we developed a method based on the computation of explicit leaf shape descriptors inspired by the criteria used in botany. This paper focuses on the characterization of the leaf contour, the extraction of its properties, and its description using botanical terms. Contour properties are investigated using the Curvature-Scale Space representation, the potential teeth explicitly extracted and described, and the margin classified into a set of inferred shape classes. Results are presented for both margin shape characterization, and leaf classification over nearly 80 tree species

Signature de la déformation entre deux images à niveaux de gris

Nous proposons une nouvelle méthode de caractérisation de la déformation entre deux images de même nature. Cette méthode est basée sur une mesure de dissimilarité entre images. La dissimilarité est calculée à partir de la distance de Baddeley et dépend d'un paramètre de réglage. La variation de la dissimilarité en fonction de ce paramètre fournit la signature de la déformation globale subie par l'image. L'analyse de la signature renseigne sur la nature de la déformation. L'analyse de l'histogramme de la différence des images de distances fournit les informations sur l'amplitude de la déformation

Guiding Active Contours for Tree Leaf Segmentation and Identification

International audienceIn the process of tree identi cation from pictures of leaves in a natural background, retrieving an accurate contour is a challenging and crucial issue. In this paper we introduce a method designed to deal with the obstacles raised by such complex images, for simple and lobed tree leaves. A rst segmentation step based on a light polygonal leaf model is first performed, and later used to guide the evolution of an active contour. Combining global shape descriptors given by the polygonal model with local curvature-based features, the leaves are then classi ed over nearly 50 tree species

A Model-Based Approach for Compound Leaves Understanding and Identification

International audienceIn this paper, we propose a specific method for the identification of compound-leaved tree species, with the aim of integrating it in an educational smartphone application. Our work is based on dedicated shape models for compound leaves, designed to estimate the number and shape of leaflets. A deformable template approach is used to fit these models and produce a high-level interpretation of the image content. The resulting models are later used for the segmentation of leaves in both plain and natural background images, by the use of multiple region-based active contours. Combined with other botany-inspired descriptors accounting for the morphological properties of the leaves, we propose a classification method that makes a semantic interpretation possible. Results are presented over a set of more than 1000 images from 17 European tree species, and an integration in the existing mobile application Folia is considered

Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification

International audienceWith the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia

Reconnaissance de gestes dynamiques de la main

Nous proposons une nouvelle méthode de reconnaissance d'un geste dynamique de la main. Un geste dynamique est caractérisé par les signatures statiques de début et fin de geste (histogramme des orientations du gradient) et par la signature dynamique (superposition des squelettes de chacune des images composant la séquence). La reconnaissance du geste se fait par une mesure de similitude entre ces signatures et des signatures d'un alphabet connu

Influence de la réduction des couleurs sur la détection des changements de plan dans les films d'animation

Dans ce papier nous proposons une technique de détection des changements de plan appliquée aux films d'animation. Cette technique est basée sur la mesure de distance entre histogrammes couleur d'images voisines. L'approche envisagée demande la définition d'une palette couleur réduite sur laquelle sont calculés les histogrammes. La détection des changements est alors effectuée par seuillage, le seuil étant déterminé automatiquement. Nous présentons des résultats obtenus avec quelques films sur lesquels une segmentation temporelle a été effectuée manuellement pour disposer d'une vérité terrain. Ces tests permettent d'évaluer l'influence du choix de la palette couleur choisie

Late Information Fusion for Multi-modality Plant Species Identification

International audienceThis article presents the participation of the ReVeS project to the ImageCLEF 2013 Plant Identification challenge. Our primary target being tree leaves, some extra effort had to be done this year to process images containing other plant organs. The proposed method tries to benefit from the presence of multiple sources of information for a same individual through the introduction of a late fusion system based on the decisions of classifiers for the different modalities. It also presents a way to incorporate the geographical information in the determination of the species by estimating their plausibility at the considered location. While maintaining its performance on leaf images (ranking 3rd on natural images and 4th on plain backgrounds) our team performed honorably on the brand new modalities with a 6th position