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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-9ad9f87725","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-cf1a19cae6","text":"\u003Cp\u003ELa vision par ordinateur est une branche de l’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/enterprise-ai/\"\u003Eintelligence artificielle\u003C/a\u003E qui entraîne des machines à interpréter et comprendre le monde visuel. Ainsi, elle permet à des ordinateurs d’analyser des images et des vidéos comme un être humain, en identifiant des objets, en reconnaissant des schémas et en tirant des conclusions de ce qu’ils voient.\u003C/p\u003E\n\u003Cp\u003ELa vision par ordinateur alimente un nombre croissant de systèmes intelligents qui \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/automl/\"\u003Eautomatisent\u003C/a\u003E des tâches qui nécessitaient autrefois un regard humain. Qu’il s’agisse de scanner des étiquettes de produits dans des entrepôts, de détecter des défauts sur des chaînes de montage ou de lire des analyses médicales, la vision par ordinateur traite des données visuelles en temps réel, puis renvoie des informations vers des systèmes stratégiques. Ainsi, elle accélère les analyses, réduit les erreurs et permet une prise de décision plus intelligente dans tous les secteurs.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-6ec18c4997","additionalClasses":"headline-decoration","type":"heading2","lines":["Définition de la vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-898887c0ab","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELa vision par ordinateur apprend à des machines à comprendre ce qu’elles voient. Elle combine l’informatique, les mathématiques et le \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emachine learning\u003C/a\u003E pour extraire du sens à partir d’images et de vidéos numériques. L’objectif n’est pas seulement de capturer des données visuelles, mais de les interpréter en identifiant ce qu’il y a sur une image, en comprenant son contexte et en utilisant ces informations pour prendre des décisions.\u003C/p\u003E\n\u003Cp\u003ECe domaine repose sur plusieurs capacités fondamentales. La reconnaissance d’images permet aux systèmes de catégoriser ce qu’ils voient : par exemple, distinguer un chat d’un chien, ou un piéton d’un panneau de signalisation. La détection d’objet va plus loin, en localisant ces éléments sur une image et en les suivant dans le temps. L’analyse des schémas complète le tout, en aidant les algorithmes à reconnaître les formes, les mouvements ou les textures récurrents qui révèlent des informations plus globales.\u003C/p\u003E\n\u003Cp\u003EContrairement au traitement d’images traditionnel, qui se concentre sur l’amélioration ou la compression des données visuelles, la vision par ordinateur cherche à comprendre. Elle se distingue également des autres branches de l’IA, comme le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/natural-language-processing/\"\u003Etraitement du langage naturel\u003C/a\u003E ou les systèmes de décision, car elle se concentre sur la façon dont les machines interprètent le monde à travers des pixels plutôt qu’avec des mots ou des chiffres.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_2060034519":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"work","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-0a325490be","additionalClasses":"headline-decoration","type":"heading2","lines":["Vision par ordinateur et intelligence artificielle"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-bb21cc2b1b","text":"\u003Cp\u003ELa vision par ordinateur est une pièce du puzzle plus vaste que forme l’\u003Ca href=\"https://www.snowflake.com/fr/product/features/cortex/\"\u003Eintelligence artificielle\u003C/a\u003E. L’IA est un vaste domaine axé sur la création de systèmes qui apprennent, raisonnent et agissent d’une manière qui nous rappelle l’intelligence humaine. Elle englobe diverses disciplines : le traitement du langage naturel, qui aide des ordinateurs à comprendre des discours et du texte ; la robotique, qui combine le mouvement mécanique avec la perception ; ou encore des systèmes de décision, qui analysent des données pour choisir des actions optimales.\u003C/p\u003E\n\u003Cp\u003ELa vision par ordinateur désigne la branche visuelle de cet écosystème. Alors que d’autres systèmes d’IA travaillent sur des mots, des chiffres ou des données structurées, la vision par ordinateur se concentre sur les pixels. Elle entraîne des modèles à extraire du sens d’entrées visuelles, en transformant des images et des vidéos brutes en informations sur lesquelles ils peuvent se baser pour prendre des mesures.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-1225ec025c","additionalClasses":"headline-decoration","type":"heading2","lines":["Fonctionnement de la vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-a919d4b316","text":"\u003Cp\u003ETout système de vision par ordinateur a pour point de départ une image. Cette image peut provenir de l’appareil photo d’un smartphone, d’un capteur industriel ou d’un satellite, mais le processus commence de la même manière : par la capture de données visuelles brutes. Avant toute analyse, le système nettoie et normalise ces données au moyen d’un prétraitement qui consiste à ajuster l’éclairage, l’échelle et le bruit, afin que les images soient prêtes à être interprétées.\u003C/p\u003E\r\n\u003Cp\u003EVient ensuite l’extraction de features : les algorithmes identifient des détails significatifs comme les bords, les couleurs, les formes ou les textures. Ces features sont ensuite comparées à des schémas appris pour classer les éléments perçus. Par exemple, un système entraîné à repérer des fissures sur un pont ou des codes-barres sur des colis apprend les signatures visuelles qui définissent chaque cible et utilise ces indices pour émettre des jugements rapides et précis.\u003C/p\u003E\r\n\u003Cp\u003ELa vision par ordinateur moderne s’appuie largement sur le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/deep-learning/\"\u003Edeep learning\u003C/a\u003E, en particulier les réseaux neuronaux convolutifs (CNN). Ces modèles apprennent automatiquement à reconnaître des features visuelles de plus en plus complexes (d’abord des bords et des lignes, puis des objets et des scènes) en traitant des jeux de données massifs d’images étiquetées. Une fois entraînés, les CNN peuvent exécuter l’inférence en temps réel, afin de reconnaître et de catégoriser instantanément les images capturées par un appareil.\u003C/p\u003E\r\n\u003Cp\u003EDe nombreuses \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/ai-applications/\"\u003Eapplications\u003C/a\u003E utilisent également des boucles de rétroaction qui permettent aux systèmes de s’améliorer au fur et à mesure. Lorsqu’un modèle commet une erreur d’identification d’un objet par exemple, la correction intègre les données d’entraînement, de façon à affiner la précision du système au fil du temps. Associés à un calcul à grande vitesse et à un déploiement sur le cloud ou à l’edge, ces modèles basés sur la rétroaction permettent à des caméras et des capteurs d’interpréter leur environnement et de réagir en quelques millisecondes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-c10b6ab5c3","additionalClasses":"headline-decoration","type":"heading2","lines":["Tâches et capacités de la vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-e5b3d92817","text":"\u003Cp\u003ELa vision par ordinateur combine plusieurs capacités qui permettent à des machines non seulement de voir, mais aussi d’interpréter ce qu’elles voient. Ces capacités s’appuient les unes sur les autres pour créer des systèmes capables de traiter des images et des vidéos, de reconnaître des schémas et de prendre des décisions éclairées en temps réel. Ces capacités incluent :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDétection et classification d’objets\u003C/h3\u003E\n\u003Cp\u003ECes capacités sont à la base de la plupart des systèmes de vision par ordinateur. La détection localise des objets dans une image, comme des voitures sur des enregistrements de la circulation ou des produits sur une étagère, tandis que la classification identifie ces objets. Ensemble, elles sont au fondement de l’automatisation dans divers domaines allant du \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/manufacturing/\"\u003Esecteur de l’industrie\u003C/a\u003E à la conduite autonome.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReconnaissance faciale et analyse des émotions\u003C/h3\u003E\n\u003Cp\u003ECes modèles cartographient des caractéristiques faciales et les comparent à des schémas enregistrés, afin de permettre des applications allant de l’authentification biométrique sécurisée à l’évaluation des opinions des clients dans les secteurs du \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/retail-consumer-goods/\"\u003Eretail\u003C/a\u003E et du divertissement.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESegmentation et annotation d’images\u003C/h3\u003E\n\u003Cp\u003ELa segmentation décompose des contenus visuels en régions plus petites et étiquetées afin que les systèmes puissent comprendre des scènes complexes. Un modèle d’imagerie médicale, par exemple, peut isoler des types de tissus pour aider les radiologues à repérer plus précisément les anomalies.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EOCR et compréhension de documents\u003C/h3\u003E\n\u003Cp\u003ELa reconnaissance optique de caractères (OCR) traduit du texte visuel (comme des factures, des identifiants ou des notes manuscrites) en données lisibles par machine. Cela permet l’automatisation du traitement de documents et de la saisie de données à grande échelle.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReconnaissance d’activité et suivi de mouvement\u003C/h3\u003E\n\u003Cp\u003ECes capacités permettent à des systèmes d’interpréter des mouvements sur des vidéos. Ainsi, ils peuvent repérer des chutes dans des établissements de santé, surveiller des flux de travail sur des chaînes de montage ou encore analyser le trafic pour améliorer la sécurité.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-21a5dd60ec","additionalClasses":"headline-decoration","type":"heading2","lines":["Applications et exemples de vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0f48e63e99","text":"\u003Cp\u003ELa vision par ordinateur fait désormais partie intégrante des opérations quotidiennes dans de nombreux secteurs. Des voitures aux cliniques, sans oublier les usines, elle traduit des données visuelles en actions concrètes. Voici comment elle est utilisée aujourd’hui :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVéhicules autonomes et analyse de la circulation\u003C/h3\u003E\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/en/news/press-releases/snowflake-drives-the-future-of-automotive-with-new-ai-powered-solutions/\"\u003Evoitures autonomes\u003C/a\u003E dépendent de la vision par ordinateur pour interpréter le monde qui les entoure. Des caméras et des capteurs fournissent en continu des données visuelles à des modèles qui détectent les piétons, lisent les panneaux de signalisation et reconnaissent les marquages au sol. Cette même technologie aide des villes à analyser la fluidité de la circulation, à optimiser la signalisation et à améliorer la sécurité routière grâce à une surveillance en temps réel.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDiagnostics médicaux et imagerie médicale\u003C/h3\u003E\n\u003Cp\u003EEn \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/healthcare-and-life-sciences/\"\u003Emédecine\u003C/a\u003E, la vision par ordinateur aide les médecins en identifiant des schémas qui pourraient échapper à un œil humain. Les algorithmes peuvent détecter des tumeurs sur des scanners, segmenter les tissus sur des IRM ou signaler des anomalies sur des rétinographies. Ces outils ne remplacent pas les cliniciens, mais leur permet plutôt d’obtenir plus rapidement un second avis concordant, de façon à accélérer le diagnostic et le traitement.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAnalyses et suivi du comportement des clients dans le retail\u003C/h3\u003E\n\u003Cp\u003ELes retailers utilisent la vision par ordinateur pour comprendre les déplacements des clients en magasin. Les caméras suivent les schémas de trafic, les interactions avec les produits et les temps d’attente pour optimiser l’agencement et le merchandising. Certains systèmes surveillent même les stocks en rayon et alertent le personnel si des produits doivent être réapprovisionnés.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDétection des défauts de fabrication\u003C/h3\u003E\n\u003Cp\u003EDes usines déploient des systèmes de vision pour repérer les défauts ou les déviations en temps réel. Des caméras positionnées le long de lignes de production filment chaque produit pour qu’il soit comparé instantanément à sa version idéale par des algorithmes. Cela permet aux industriels de détecter rapidement les problèmes, de réduire les pertes et de maintenir une qualité constante à grande échelle.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESystèmes de sécurité et de surveillance\u003C/h3\u003E\n\u003Cp\u003ELa vision par ordinateur alimente l’infrastructure de sécurité moderne, de la reconnaissance faciale dans les aéroports à la détection de mouvement sur les caméras intelligentes. Ces systèmes analysent en continu des images pour faire la différence entre des mouvements de routine et des menaces potentielles. Ils peuvent ainsi déclencher des alertes dès qu’ils détectent une activité inhabituelle.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ETraitement de documents et OCR\u003C/h3\u003E\n\u003Cp\u003EDes entreprises s’appuient sur la vision par ordinateur pour convertir leurs documents, reçus et formulaires manuscrits scannés en données structurées. Les outils OCR extraient et organisent les informations afin qu’elles puissent être recherchées, validées et intégrées directement aux flux de travail de l’entreprise, sans qu’il soit nécessaire de saisir manuellement les données.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_289473020":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-ed3198bd8b","additionalClasses":"headline-decoration","type":"heading2","lines":["Avantages de la vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-44513da6bc","text":"\u003Cp\u003ELa vision par ordinateur permet de travailler plus intelligemment et plus rapidement. En effet, cette technologie apporte de nombreux avantages tangibles en termes de précision, de vitesse et d’expérience utilisateur. Voici quelques-uns des plus grands avantages de cette technologie :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAutomatisation et efficacité améliorées\u003C/h3\u003E\n\u003Cp\u003EAvec la vision par ordinateur, les travailleurs humains n’ont plus besoin d’effectuer des tâches visuelles répétitives et peuvent alors se concentrer sur des tâches à plus forte valeur ajoutée. La vision par ordinateur simplifie toutes sortes d’opérations, des chaînes de montage aux plateformes logistiques, et améliore ainsi le rendement tout en réduisant les coûts de main-d’œuvre.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAmélioration de la précision des tâches visuelles\u003C/h3\u003E\n\u003Cp\u003ELes modèles d’IA entraînés sur des jeux de données massifs peuvent détecter des détails subtils que des utilisateurs pourraient manquer, ce qui permet d’obtenir des résultats plus constants et de réduire les erreurs. Cette précision améliore le contrôle qualité et aide différents secteurs à respecter des normes de conformité ou de sécurité plus strictes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ECapacités de prise de décision en temps réel\u003C/h3\u003E\n\u003Cp\u003EEn traitant instantanément les données visuelles, la vision par ordinateur permet aux entreprises d’exploiter les informations au fil des événements. En effet, les entreprises capables de détecter et réagir en quelques secondes peuvent prévenir les accidents, réduire les temps d’arrêt et améliorer leur maîtrise de la situation.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDéploiement évolutif sur différentes plateformes\u003C/h3\u003E\n\u003Cp\u003ELa vision par ordinateur s’exécute partout, des appareils à l’edge (comme les smartphones et les capteurs en usine) aux systèmes d’analyse basés sur le cloud. Cette flexibilité permet aux entreprises de commencer petit, puis d’étendre cette technologie à d’autres produits, sites ou régions sans avoir à reconstruire leurs systèmes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERéduction des erreurs humaines\u003C/h3\u003E\n\u003Cp\u003ELes systèmes de vision automatisés maintiennent des performances constantes, pour moins de supervision et plus de fiabilité dans les environnements où la précision est essentielle. Contrairement à des observateurs humains, ils ne se fatiguent jamais et ne perdent pas leur concentration, garantissant ainsi des résultats stables, quelle que soit leur durée de fonctionnement.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAmélioration de l’expérience client et utilisateur\u003C/h3\u003E\n\u003Cp\u003ELa vision par ordinateur permet de créer des interactions plus fluides et plus personnalisées, telles que des achats sans passage en caisse et des interfaces adaptatives. Lorsque les systèmes sont capables de reconnaître les comportements et le contexte, ils peuvent anticiper les besoins et éliminer les frictions dans les expériences quotidiennes.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-c7c908f82f","additionalClasses":"headline-decoration","type":"heading2","lines":["Défis de la vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-19515fb295","text":"\u003Cp\u003EMalgré toutes ses promesses, la vision par ordinateur ne coule pas de source. Pour construire des systèmes fiables, il convient de surmonter quelques obstacles persistants en matière de qualité des données, de performances et d’intégration. Voici les principaux :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVariabilité de la qualité d’image et de l’éclairage\u003C/h3\u003E\n\u003Cp\u003ELes changements d’éclairage, d’angle de caméra ou de résolution peuvent fausser les résultats des systèmes de détection. Un modèle entraîné sur des photos claires et bien éclairées peut se retrouver en difficulté lorsque les conditions évoluent (dans des entrepôts peu lumineux ou sous un soleil éblouissant, par exemple). L’homogénéité des données d’entrée représente donc un défi constant.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EBesoins élevés en calcul\u003C/h3\u003E\n\u003Cp\u003EL’exécution de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emodèles de deep learning\u003C/a\u003E pour des analyses en temps réel nécessite un matériel puissant et une grande consommation d’énergie. L’entraînement et l’inférence à grande échelle nécessitent souvent des GPU ou des puces spécialisées, ce qui peut entraîner une hausse des coûts d’infrastructure et d’exploitation.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EManque de données d’entraînement étiquetées\u003C/h3\u003E\n\u003Cp\u003EEn l’absence de jeux de données diversifiés et bien annotés, il est difficile de généraliser les modèles et de les adapter à de nouvelles conditions. Beaucoup de main-d’œuvre est nécessaire pour collecter et étiqueter des exemples en quantité suffisante, et les lacunes dans les données conduisent souvent à des systèmes fragiles qui fonctionnent mal en dehors des conditions idéales.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EBiais et équité dans la reconnaissance visuelle\u003C/h3\u003E\n\u003Cp\u003ELes modèles entraînés sur des données déséquilibrées peuvent se tromper ou se montrer moins performants pour certains groupes démographiques. La correction de ces biais implique de repenser la composition des jeux de données et d’intégrer des processus de test et d’examen pour détecter rapidement les disparités.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIntégration avec des systèmes hérités\u003C/h3\u003E\n\u003Cp\u003EBien souvent, les infrastructures plus anciennes n’offrent pas les performances ou la compatibilité nécessaires pour les charges de travail d’IA modernes. Pour connecter de nouvelles plateformes de vision par ordinateur à des bases de données ou des outils opérationnels existants, il peut être nécessaire de refondre les flux de travail ou d’ajouter un middleware pour combler le fossé.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"types","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-028f1a16bf","additionalClasses":"headline-decoration","type":"heading2","lines":["L’avenir de la vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-1c793142cc","text":"\u003Cp\u003ELa vision par ordinateur évolue rapidement grâce aux nouveautés techniques et matérielles de l’IA qui la rendent plus rapide, plus intelligente et plus accessible. Ces tendances émergentes nous laissent entrevoir la direction que devrait prendre cette technologie :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EModélisation spatiale et apprentissage multimodal basés sur l’IA\u003C/h3\u003E\n\u003Cp\u003ELes systèmes de demain combineront des données visuelles avec d’autres entrées sensorielles (audio, texte, profondeur, etc.) afin de mieux comprendre leur environnement.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVision en temps réel sur les appareils à l’edge\u003C/h3\u003E\n\u003Cp\u003EÀ mesure que les réseaux neuronaux deviennent plus légers et les puces plus efficaces, les analyses se déplacent du cloud vers l’edge.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ECartographie 3D et réalité augmentée\u003C/h3\u003E\n\u003Ch3\u003E \u003C/h3\u003E\n\u003Cp\u003ELa vision par ordinateur ne se limite plus aux images planes. Elle comprend désormais des images 3D et réunit les mondes physique et numérique.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EGénération de données synthétiques pour l’entraînement\u003C/h3\u003E\n\u003Cp\u003ELes développeurs utilisent des images simulées ou générées par IA pour entraîner des modèles et pallier les pénuries de données.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDémocratisation des outils de vision pour les utilisateurs non techniques\u003C/h3\u003E\n\u003Cp\u003ELes plateformes no-code et low-code rendent la vision par ordinateur accessible aux utilisateurs métiers sans véritable formation, pour plus d’innovation et d’accessibilité.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[]},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[]},"container_copy_2083166428":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conslusion","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-8c7aab3a4b","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusion"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-b29c4eab0e","text":"\u003Cp\u003ELa vision par ordinateur est au cœur de la révolution actuelle de l’IA. En permettant à des machines de voir et d’interpréter le monde, elle transforme immédiatement les données visuelles en informations exploitables. Les technologies de base utilisées pour la détection d’objets, la reconnaissance de schémas et l’\u003Ca href=\"https://www.snowflake.com/fr/product/analytics/\"\u003Eanalyse en temps réel\u003C/a\u003E redéfinissent les modes de fonctionnement des différents secteurs, avec une automatisation plus intelligente, plus précise et plus évolutive.\u003C/p\u003E\n\u003Cp\u003EDans des secteurs tels que la santé, le retail, l’industrie ou encore \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/travel-hospitality/\"\u003Eles transports\u003C/a\u003E, la vision par ordinateur améliore la prise de décision et simplifie des flux de travail autrefois dépendants d’une intervention humaine. Au fil de leur évolution, ces systèmes ne se contentent pas d’analyser ce qu’ils voient : ils aident également les entreprises à anticiper l’avenir.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_copy_20831":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-7f9d6c3535","additionalClasses":"headline-decoration","type":"heading2","lines":["Questions fréquentes sur la vision par ordinateur"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-4ef0047900","showDivider":false,"accordionItemsList":[{"title":"Que sont les 3 R de la vision par ordinateur ?","richText":"\u003Cp\u003ELes 3 R (reconnaissance, reconstruction et réorganisation) décrivent la façon dont les systèmes de vision comprennent la signification d’images. La reconnaissance consiste à nommer ce qu’il y a sur l’image. La reconstruction reconstitue des formes 3D ou la disposition d’une scène à partir d’images 2D. La réorganisation regroupe les pixels en éléments significatifs au profit de la rapidité et de la précision d’autres étapes. La plupart des systèmes combinent les trois.\u003C/p\u003E\n"},{"title":"Quels outils sont utilisés dans le développement de la vision par ordinateur ?","richText":"\u003Cp\u003ELes ingénieurs utilisent généralement OpenCV pour les opérations sur les images et TensorFlow ou PyTorch pour entraîner et exécuter des modèles. Les déploiements sont effectués sur des services cloud comme Azure ou AWS, ou sur des appareils à l’edge lorsqu’il faut faire attention à la latence. Les data clouds tels que Snowflake aident à gérer les données d’entraînement, les features et les pipelines qui alimentent ces modèles.\u003C/p\u003E\n"},{"title":"Quels sont les algorithmes de vision par ordinateur les plus courants ?","richText":"\u003Cp\u003ELes réseaux neuronaux convolutifs (CNN) permettent des tâches telles que la reconnaissance et la détection d’objets. Des méthodes classiques (telles que Haar Cascades) sont encore utilisées pour les détecteurs de visage légers et le flux optique suit les mouvements sur les vidéos. De nombreux systèmes de production combinent ces approches pour maintenir un équilibre entre vitesse et précision.\u003C/p\u003E\n"},{"title":"Quelle est la différence entre le traitement d’images et la vision par ordinateur ?","richText":"\u003Cp\u003ELe traitement d’image améliore une image, par exemple en éliminant le bruit sur une photo ou en ajustant le contraste. La vision par ordinateur interprète l’image : elle identifie les objets, segmente les régions et déclenche des actions en fonction de ce qu’elle « voit ».\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customers","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-3cac717770","additionalClasses":"headline-decoration","type":"heading2","lines":["Clients qui utilisent Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-12d6eb863e","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-966f4c2a7d","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-a8d3032db9","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Simon Data fait évoluer son marketing avec des agents d’IA composables basés sur Snowflake Cortex AI"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/simon-data/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire le témoignage"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fc3568f8-1374-44a7-b560-0b1ea179bf05/simon-data-customer-card%25402x.jpg?quality=85&preferwebp=true","alt":"merkle logo","lazyEnabled":true,"height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EEn s’appuyant sur Snowflake pour développer son IA agentique, Simon Data aide les spécialistes du marketing à doper leurs revenus en proposant une personnalisation contextuelle à grande échelle, le tout sans déplacer de données ni compromettre la 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