A global method for music symbol recognition in typeset music sheets

International audienceThis paper presents an optical music recognition (OMR) system that can automatically recognize the main musical symbols of a scanned paper-based music score. Two major stages are distinguished: the first one, using low-level pre-processing, detects the isolated objects and outputs some hypotheses about them; the second one has to take the final correct decision, through high-level processing including contextual information and music writing rules. This article exposes both stages of the method: after explaining in detail the first one, the symbol analysis process, it shows through first experiments that its outputs can efficiently be used as inputs for a high-level decision process

Segmentation and size estimation of tomatoes from sequences of paired images

International audienceIn this paper, we present a complete system to monitor the growth of tomatoes from images acquired in open fields. This is a challenging task because of the severe occlusion and poor contrast in the images. We approximate the tomatoes by spheres in the 3D space, hence by ellipses in the image space. The tomatoes are first identified in the images using a segmentation procedure. Then, the size of the tomatoes is measured from the obtained segmentation and camera parameters. The shape information combined with temporal information, given the limited evolution from an image to the next one, is used throughout the system to increase the robustness with respect to occlusion and poor contrast. The segmentation procedure presented in this paper is an extension of our previous work based on active contours. Here, we present a method to update the position of the tomato by comparing the SIFT descriptors computed at predetermined points in two consecutive images. This leads to a very accurate estimation of the tomato position, from which the entire segmentation procedure benefits. The average error between the automatic and manual segmentations is around 4 % (expressed as the percentage of tomato size) with a good robustness with respect to occlusion (up to 50 %). The size estimation procedure was evaluated by calculating the size of tomatoes under a controlled environment. In this case, the mean percentage error between the actual radius and the estimated size is around 2.35 % with a standard deviation of 1.83 % and is less than 5 % in most (91 %) cases. The complete system was also applied to estimate the size of tomatoes cultivated in open fields

Eyelid Localization for Iris Identification

This article presents a new eyelid localization algorithm based on a parabolic curve fitting. To deal with eyelashes, low contrast or false detection due to iris texture, we propose a two steps algorithm. First, possible edge candidates are selected by applying edge detection on a restricted area inside the iris. Then, a gradient maximization is applied along every parabola, on a larger area, to refine parameters and select the best one. Experiments have been conducted on a database of 151 iris that have been manually segmented. The performance evaluation is carried out by comparing the segmented images obtained by the proposed method with the manual segmentation. The results are satisfactory in more than 90% of the cases

Segmentation of tumor vessels based on parallel double snakes including region information

International audience— In this paper, we address the problem of the seg-mentation of vessels in images of mouse tumors, with an efficient algorithm that minimizes the user's intervention. For each vessel, two points delimiting its extremities have to be selected. Then, a line inside the vessel is automatically determined based on a Dijkstra-type algorithm. Finally, an original active contour model combining both parallel double snakes and region criteria aims at finding the borders of the vessel. Our segmentation algorithm provides numerical models of tumor vessels, suitable for the simulation of blood and contrast agent flow

Une méthode globale pour la reconnaissance de partitions musicales

Cet article présente une méthode de reconnaissance automatique de partitions musicales. L'objectif est de permettre l'intégration d'un maximum de connaissances contextuelles, afin de réaliser une interprétation globale de haut niveau de l'ensemble de la partition. La méthode exposée procède en deux temps : la première étape analyse individuellement les objets et fournit des hypothèses de reconnaissance à la seconde étape qui, intégrant les règles d'écriture musicale, aboutit à une décision globale cohérente

Intégration de connaissances a priori dans les systèmes d’analyse d’images : Applications en analyse de documents, biométrie et ophtalmologie

This document summarizes the research I have been conducting since 2000 in the following application domains: optical recognition of music scores (OMR), biometrics, medical imaging in ophtalmology.Ce document synthétise les travaux de recherche que j’ai menés depuis 2000 , dans les domaines suivants : reconnaissance optique de partitions musicales numérisées (OMR, Chapitre 2), identification biométrique par l’iris de l’œil (Chapitre 3), imagerie dans le domaine de l’ophtalmologie (Chapitre 4), avec la segmentation d’images OCT de rétines saines (Section 4.2) ou pathologiques (Section 4.3), le comptage de photorécepteurs dans des images d’optique adaptative (Section 4.4), la segmentation des vaisseaux rétiniens dans des images acquises par rétinographie ou par optique adaptative (Section 4.5). Ces différents projets sont résumés dans la section 1.3.1. Ils m’ont permis de travailler à plusieurs niveaux de la chaîne d’interprétation des images : segmentation, reconnaissance (classification), interprétation de haut niveau. Ils ont tous abouti à des systèmes complets, exploitables, évalués par rapport à l’état de l’art. Bien que les contextes applicatifs soient très différents, une composante commune a structuré mes recherches : la nécessité, bien connue dans le domaine du traitement des images, de modéliser et d’intégrer des informations a priori dans les systèmes de reconnaissance, pour en améliorer la fiabilité et la précision (Section 1.3.2)

Robust and Adaptive OMR System Including Fuzzy Modeling, Fusion of Musical Rules, and Possible Error Detection

This paper describes a system for optical music recognition (OMR) in case of monophonic typeset scores. After clarifying the difficulties specific to this domain, we propose appropriate solutions at both image analysis level and high-level interpretation. Thus, a recognition and segmentation method is designed, that allows dealing with common printing defects and numerous symbol interconnections. Then, musical rules are modeled and integrated, in order to make a consistent decision. This high-level interpretation step relies on the fuzzy sets and possibility framework, since it allows dealing with symbol variability, flexibility, and imprecision of music rules, and merging all these heterogeneous pieces of information. Other innovative features are the indication of potential errors and the possibility of applying learning procedures, in order to gain in robustness. Experiments conducted on a large data base show that the proposed method constitutes an interesting contribution to OMR

Reconnaissance de partitions musicales par modélisation floue et intégration de règles musicales

Cet article propose une méthode d'interprétation de haut niveau de partitions musicales fondée sur la théorie mathématique des ensembles flous et des possibilités. L'idée directrice est de prendre en compte les ambiguïtés et les imprécisions qui subsistent à l'issue de l'analyse individuelle des symboles, et d'intégrer les règles de notation musicale, afin d'aboutir à une décision globale cohérente. Nous montrerons l'intérêt de ce formalisme en l'appliquant notamment aux altérations

Reconnaissance de partitions musicales par modélisation floue des informations extraites et des règles de notation

Nous présentons dans cette thèse une méthode complète de reconnaissance de partitions musicales imprimées, dans le cas monodique. Le système procède en deux phases distinctes : la segmentation et l'analyse des symboles (essentiellement par corrélation), conçues pour surmonter les difficultés liées aux interconnexions et aux défauts d'impression, aboutissant à des hypothèses de reconnaissance. L'interprétation de haut niveau, fondée sur une modélisation floue des informations extraites de l'image et des règles de notation, menant à la décision. Dans cette approche, la décision est reportée tant que le contexte n'est pas entièrement connu. Toutes les configurations d hypothèses sont successivement évaluées, et la plus cohérente est retenue, par optimisation de tous les critères. Le formalisme utilisé, fondé sur la théorie des ensembles flous et des possibilités, permet de prendre en compte les différentes sources d'imprécision et d'incertitude, ainsi que la souplesse et la flexibilité de l'écriture musicale. Afin de gagner en fiabilité, nous proposons également des méthodes d'indication automatique des erreurs potentielles de reconnaissance, ainsi qu'une procédure d'apprentissage, optimisant les paramètres du système pour le traitement d'une partition particulière. Les performances obtenues sur une large base de données ont permis de montrer l'intérêt de la méthode proposée.This thesis deals with Optical Music Recognition (OMR), in case of monophonic typeset music. The proposed method relies on two separated stages : the symbol segmentation and analysis step, designed in order to deal with common printing defects and numerous symbol interconnexions. A set of recognition hypotheses is generated, based on correlation scores with class reference models. A high-level interpretation step, based on the fuzzy modeling of the extracted information and of musical rules, leading to the decision. In this approach, the decision is delayed until the entirely context can be evaluated. All the hypothesis configurations are considered, and the decision is taken through a global consistency evaluation. This high-level interpretation step relies on the fuzzy sets and possibility framework, since it allows dealing with symbol variability, the flexibility and the imprecision of music rules, and merging all these heterogeneous pieces of information. Other innovative features are the indication of potential errors, and the possibility of applying learning procedures, in order to gain in robustness. Experiments conducted on a large data base show that the proposed method constitutes an interesting contribution to OMR.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Normalization of series of fundus images to monitor the geographic atrophy growth in dry age-related macular degeneration

International audienceBackground and Objective: Age-related macular degeneration (ARMD) is a degenerative disease that affects the retina, and the leading cause of visual loss. In its dry form, the pathology is characterized by the progressive, centrifugal expansion of retinal lesions, called geographic atrophy (GA). In infrared eye fundus images, the GA appears as localized bright areas and its growth can be observed in series of images acquired at regular time intervals. However, illumination distortions between the images make impossible the direct comparison of intensities in order to study the GA progress. Here, we propose a new method to compensate for illumination distortion between images.Methods: We process all images of the series so that any two images have comparable gray levels. Our approach relies on an illumination/reflectance model. We first estimate the pixel-wise illumination ratio between any two images of the series, in a recursive way; then we correct each image against all the others, based on those estimates. The algorithm is applied on a sliding temporal window to cope with large changes in reflectance. We also propose morphological processing to suppress illumination artefacts.Results: The corrected illumination function is homogeneous in the series, enabling the direct comparison of grey-levels intensities in each pixel, and so the detection of the GA growth between any two images. To demonstrate that, we present numerous experiments performed on a dataset of 18 series (328 images), manually segmented by an ophthalmologist. First, we show that the normalization preprocessing dramatically increases the contrast of the GA growth areas. Secondly, we apply segmentation algorithms derived from Otsu’s thresholding to detect automatically the GA total growth and the GA progress between consecutive images. We demonstrate qualitatively and quantitatively that these algorithms, although fully automatic, unsupervised and basic, already lead to interesting segmentation results when applied to the normalized images. Colored maps representing the GA evolution can be derived from the segmentations.Conclusion: To our knowledge, the proposed method is the first one which corrects automatically and jointly the illumination inhomogeneity in a series of fundus images, regardless of the number of images, the size, shape and progression of lesion areas. This algorithm greatly facilitates the visual interpretation by the medical expert. It opens up the possibility of treating automatically each series as a whole (not just in pairs of images) to model the GA growth