De l'appariement de graphes symboliques à l'appariement de graphes numériques : Application à la reconnaissance de symboles

Les représentations sous forme de graphes structurels ont été appliquées dans un grand nombre de problèmes en vision par ordinateur et en reconnaissance de formes. Néanmoins, lors de l'étape d'appariement de graphes, les algorithmes classiques d'isomorphisme de graphes sont peu performants quand l'image est dégradée par du bruit ou des distorsions vectorielles. Cet article traite de la reconnaissance de symboles graphiques grâce à la formulation d'une nouvelle mesure de similarité entre leur représentation sous forme de graphes étiquetés. Dans l'approche proposée, les symboles sont d'abord décomposés en primitives structurelles et un graphe attribué est alors généré pour décrire chaque symbole. Les nœuds du graphe représentent les primitives structurelles tandis que les arcs décrivent les relations topologiques entre les primitives. L'utilisation d'attributs numériques pour caractériser les primitives et leurs relations permet d'allier précision et, invariance à la rotation et au changement d'échelle. Nous proposons également une nouvelle technique d'appariement de graphes basée sur notre fonction de similarité qui utilise les valeurs numériques des attributs pour produire un score de similarité. Cette mesure de similarité a de nombreuses propriétés intéressantes comme un fort pouvoir de discrimination, une invariance aux transformations affines et une faible sensibilité au bruit

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Pattern recognition and complex graphic symbols recognition in documents images

Ce travail de thèse se situe à la croisée de trois thématiques de recherche : la mise en place de représentations structurelles pour décrire le contenu d’images de documents, la reconnaissance structurelle des formes et graphiques complexes et la localisation des symboles dans les images de documents. Pour répondre aux problématiques de l’analyse d’images de documents, nous avons choisi d’utiliser les graphes comme outils de représentation des contenus des images. La nouvelle représentation obtenue exploite un graphe multi-primitive et multi-attribut améliorant à la fois la tâche de localisation mais aussi la tâche de reconnaissance de formes graphiques contenues dans les documents. Une nouvelle approche générique et automatique est également présentée pour la localisation des symboles graphiques dans les images de documents. Notre approche de localisation des symboles nécessite un minimum de connaissances a priori sur les domaines ou sur le type de symboles présents dans les images. Concernant l’étape de reconnaissance, nous présentons trois stratégies originales pour la mise en correspondance de graphes, combinant les approches structurelle et statistique. Elles aident à la résolution du problème de complexité et évitent un temps de calcul exponentiel intolérable. Les nouvelles techniques d’appariement de graphes que nous proposons sont basées sur des fonctions de similarité qui tilisent aussi bien des valeurs numériques que symboliques pour produire un score. Ces mesures de similarité ont de nombreuses propriétés intéressantes comme un fort pouvoir discriminant, une invariance aux transformations affines et une faible sensibilité au bruit.This thesis presents our contributions related to three major research areas in the field of document image analysis i.e., structural representation of documents images, spotting symbols in graphical documents and symbols recognition. We proposed to represent the contents of the document images using multi-attributed graphs, which not only improves the task of symbols spotting, but also the task of symbols recognition. We present a new generic and automatic approach for the purpose of spotting symbols in graphical documents. Our approach to locate symbols requires minimum priori knowledge about the type of document or the type of symbols found in these documents. Concerning symbol recognition we present three new strategies combining structural and statistical approaches. The proposed approaches helped to solve the problem of time and space complexity and offers robustness against noise and distortion present in images. The new graph matching techniques that we are proposing are based on similarity function that uses both numerical and symbolic values of the nodes and edges attributes of the graphs to produce a score of similarity between two graphs. These similarity measures have many interesting properties such as a strong discriminating power, nvariance to affine transformations, and low sensitivity to noise

Reconnaissance de symboles graphiques à partir de graphes Etiquetés

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Graphic Symbol recognition using flexible matching of attributed relational graphs

International audienceMany methods of graphics recognition have been developed throughout the years for the recognition of pre-segmented graphics symbols but very few techniques achieved the objective of symbol spotting and recognition together in a generic case. To go one step forward through this objective, this paper presents an original solution for symbol spotting using a graph representation of graphical documents. The proposed strategy has two main step. In the first step, a graph based representation of a document image is generated that includes selection of description primitives (nodes of the graph) and organisation of these features (edges). In the second step the graph is used to spot interesting parts of the image that potentially correspond to symbols. The sub-graphs associated to selected zones are then submitted to a graph matching algorithm in order to take the final decision and to recognize the class of the symbol. The experimental results obtained on different types of documents demonstrates that the system can handle different types of images without any modification

Graph Based Shapes Representation and Recognition

International audienceIn this paper, we propose to represent shapes by graphs. Based on graphic primitives extracted from the binary images, attributed relational graphs were generated. Thus, the nodes of the graph represent shape primitives like vectors and quadrilaterals while arcs describing the mutual primitives relations. To be invariant to transformations such as rotation and scaling, relative geometric features extracted from primitives are associated to nodes and edges as attributes. Concerning graph matching, due to the fact of NP-completeness of graph-subgraph isomorphism, a considerable attention is given to different strategies of inexact graph matching. We also present a new scoring function to compute a similarity score between two graphs, using the numerical values associated to the nodes and edges of the graphs. The adaptation of a greedy graph matching algorithm with the new scoring function demonstrates significant performance improvements over traditional exhaustive searches of graph matching

Combination of Symbolic and Statistical Features for Symbols Recognition

International audienceIn this article, we have tried to explore a new hybrid approach which well integrates the advantages of structural and statistical approaches and avoids their weaknesses. In the proposed approach, the graphic symbols are first segmented into high-level primitive like quadrilaterals. Then, a graph is built by utilizing these quadrilaterals as nodes and their spatial relationships as edges. Additional information like relative length of the quadrilaterals and their relative angles with neighbouring quadrilaterals are associated as attributes to the nodes and edges of the graph respectively. However, the observed graphs are subject to deformations due to noise and/or vectorial distortion (in case of hand-drawn images) hence differs somewhat from their ideal models by either missing or extra nodes and edges appearance. Therefore, we propose a method that computes a measure of similarity between two given graphs instead of looking for exact isomorphism. The approach is based on comparing feature vectors extracted from the graphs. The idea is to use features that can be quickly computed from a graph on the one hand, but are, on the other hand, effective in discriminating between the various graphs in the database. The nearest neighbour rule is used as a classifier due to its simplicity and good behaviou