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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/it/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",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-f06fa0acaa","type":"heading2","lines":["Presentazione"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-6dbd28e827","text":"\u003Cp\u003ELa computer vision è un ramo dell’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/enterprise-ai/\"\u003Eintelligenza artificiale\u003C/a\u003E che addestra le macchine a interpretare e comprendere il mondo visivo. Consente ai computer di analizzare immagini e video come fa l’essere umano, identificando oggetti, riconoscendo pattern e traendo conclusioni da ciò che “vedono”.\u003C/p\u003E\n\u003Cp\u003ELa computer vision alimenta un numero crescente di sistemi intelligenti che \u003Ca href=\"https://www.snowflake.com/it/fundamentals/automl/\"\u003Eautomatizzano\u003C/a\u003E attività che prima dipendevano dall’occhio umano. Dalla scansione delle etichette dei prodotti nei magazzini al rilevamento di difetti sulle linee di produzione o alla lettura di esami medici, la computer vision elabora dati visivi in tempo reale e riporta insight ai sistemi aziendali. Il risultato è un’analisi più rapida, meno errori e decisioni più intelligenti in tutti i settori.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-c7f5ac8c0f","additionalClasses":"headline-decoration","type":"heading2","lines":["Che cos’è la computer vision?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-b683abd98f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIn sostanza, la computer vision insegna alle macchine a dare un senso a ciò che vedono. Combina informatica, matematica e \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emachine learning\u003C/a\u003E per estrarre significato da immagini e video digitali. L’obiettivo non è solo acquisire contenuti visivi, ma interpretarli: identificare cosa c’è in un’immagine, comprenderne il contesto e agire in base a queste informazioni.\u003C/p\u003E\n\u003Cp\u003EIl campo si basa su diverse capacità fondamentali. Il riconoscimento delle immagini permette ai sistemi di classificare ciò che vedono, ad esempio distinguendo un gatto da un cane o un pedone da un segnale stradale. Il rilevamento degli oggetti va oltre, localizzando questi elementi all’interno dell’immagine e tracciandoli nel tempo. L’analisi dei pattern collega il tutto, aiutando gli algoritmi a riconoscere forme, movimenti o texture ricorrenti che rivelano insight più ampi.\u003C/p\u003E\n\u003Cp\u003EA differenza dell’elaborazione tradizionale delle immagini, che si concentra sul miglioramento o sulla compressione dei dati visivi, la computer vision punta alla comprensione. Si distingue anche da altri rami dell’AI, come il \u003Ca href=\"https://www.snowflake.com/en/fundamentals/natural-language-processing/\"\u003Enatural language processing\u003C/a\u003E o i sistemi decisionali, perché si concentra su come le macchine interpretano il mondo attraverso i pixel, non attraverso parole o numeri.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_2060034519":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-da5cf325be","additionalClasses":"headline-decoration","type":"heading2","lines":["Computer vision vs intelligenza artificiale"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-9d640a519c","text":"\u003Cp\u003ELa computer vision è un tassello del più ampio ecosistema dell’\u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003Eintelligenza artificiale\u003C/a\u003E. L’AI è un campo vasto che mira a creare sistemi in grado di apprendere, ragionare e agire in modi che associamo all’intelligenza umana. Include discipline come il natural language processing, che aiuta i computer a comprendere parlato e testo; la robotica, che combina movimento meccanico e percezione; e sistemi decisionali che analizzano i dati per scegliere le azioni migliori.\u003C/p\u003E\n\u003Cp\u003ELa computer vision rappresenta il ramo visivo di questo ecosistema. Mentre altri sistemi di AI lavorano con parole, numeri o dati strutturati, la computer vision si concentra sui pixel. Addestra modelli a estrarre significato da input visivi, trasformando immagini e video grezzi in informazioni su cui possono agire.\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_":{"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"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-901bd93cf5","additionalClasses":"headline-decoration","type":"heading2","lines":["Come funziona la computer vision"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-453685c97f","text":"\u003Cp\u003EOgni sistema di computer vision inizia da un’immagine. Può provenire dalla fotocamera di uno smartphone, da un sensore industriale o da un feed satellitare, ma il processo inizia sempre nello stesso modo: acquisendo dati visivi grezzi. Prima di qualsiasi analisi, il sistema pulisce e standardizza questi dati tramite pre-processing, regolando illuminazione, scala e rumore per rendere le immagini pronte per l’interpretazione.\u003C/p\u003E\n\u003Cp\u003ESegue l’estrazione delle caratteristiche, in cui gli algoritmi individuano dettagli significativi come bordi, colori, forme o texture. Queste caratteristiche vengono poi confrontate con pattern appresi per classificare ciò che viene osservato. Ad esempio, un sistema addestrato a individuare crepe in una soletta di un ponte o codici a barre sui pacchi apprende le “firme” visive che definiscono ogni target e utilizza questi segnali per formulare valutazioni rapide e accurate.\u003C/p\u003E\n\u003Cp\u003ELa computer vision moderna si basa in larga misura sul deep learning, in particolare sulle reti neurali convoluzionali (CNN). Questi modelli apprendono automaticamente caratteristiche visive sempre più complesse, prima bordi e linee, poi oggetti e scene, elaborando enormi dataset di immagini etichettate. Una volta addestrate, le CNN possono eseguire inferenza in tempo reale, riconoscendo e classificando all’istante ciò che viene ripreso da una fotocamera.\u003C/p\u003E\n\u003Cp\u003EMolte \u003Ca href=\"https://www.snowflake.com/it/fundamentals/ai-applications/\"\u003Eapplicazioni\u003C/a\u003E utilizzano anche cicli di feedback che consentono ai sistemi di migliorare nel tempo. Quando un modello commette un errore, ad esempio identificando in modo errato un oggetto, la correzione diventa nuovo dato di addestramento, affinando l’accuratezza del sistema nel tempo. In combinazione con calcolo ad alte prestazioni e deployment su cloud o edge, questi modelli guidati dal feedback consentono a videocamere e sensori di interpretare l’ambiente e reagire in millisecondi.\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_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-76df256656","additionalClasses":"headline-decoration","type":"heading2","lines":["Attività e capacità della computer vision"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cac9a1c070","text":"\u003Cp\u003ELa computer vision combina più capacità che consentono alle macchine non solo di vedere, ma anche di interpretare ciò che vedono. Ogni capacità si basa sulle altre per creare sistemi che elaborano immagini e video, riconoscono pattern e prendono decisioni informate in tempo reale. Tra queste capacità:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERilevamento e classificazione degli oggetti\u003C/h3\u003E\n\u003Cp\u003ESono le basi della maggior parte dei sistemi di computer vision. Il rilevamento localizza gli oggetti in un’immagine, ad esempio auto in riprese del traffico o prodotti su uno scaffale, mentre la classificazione identifica che cosa sono quegli oggetti. Insieme, costituiscono la base dell’automazione in ambiti che vanno dal \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/\"\u003Emanifatturiero\u003C/a\u003E alla guida autonoma.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERiconoscimento facciale e analisi delle emozioni\u003C/h3\u003E\n\u003Cp\u003EQuesti modelli mappano i punti di riferimento del volto e li confrontano con pattern memorizzati, abilitando applicazioni che spaziano dall’autenticazione biometrica sicura alla misurazione del sentiment dei clienti in contesti \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/retail-consumer-goods/\"\u003Eretail\u003C/a\u003E e di intrattenimento.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESegmentazione e annotazione delle immagini\u003C/h3\u003E\n\u003Cp\u003ELa segmentazione suddivide i contenuti visivi in regioni più piccole ed etichettate, così che i sistemi possano comprendere scene complesse. Un modello di imaging medico, ad esempio, può isolare i tipi di tessuto in una scansione per aiutare i radiologi a individuare anomalie con maggiore precisione.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EOCR e comprensione dei documenti\u003C/h3\u003E\n\u003Cp\u003EL’OCR (riconoscimento ottico dei caratteri) trasforma il testo visivo, come fatture, documenti d’identità o appunti scritti a mano, in dati leggibili dalle macchine. Questo consente elaborazione automatizzata dei documenti e inserimento dati su larga scala.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERiconoscimento delle attività e tracciamento del movimento\u003C/h3\u003E\n\u003Cp\u003EQueste capacità permettono ai sistemi di interpretare il movimento tra i fotogrammi di un video. Possono identificare una caduta in un contesto sanitario, monitorare i workflow su una linea di assemblaggio o analizzare i flussi di traffico per migliorare la sicurezza.\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":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-daa4dac877","additionalClasses":"headline-decoration","type":"heading2","lines":["Applicazioni ed esempi di computer vision"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-61d61b9299","text":"\u003Cp\u003EOggi la computer vision è integrata nelle operations quotidiane di numerosi settori. Dalle auto alle cliniche, fino agli stabilimenti produttivi, trasforma i dati visivi in azioni concrete. Ecco come viene utilizzata oggi:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EVeicoli a guida autonoma e analisi del traffico\u003C/h3\u003E\r\n\u003Cp\u003EI \u003Ca href=\"https://www.snowflake.com/en/news/press-releases/snowflake-drives-the-future-of-automotive-with-new-ai-powered-solutions/\"\u003Eveicoli a guida autonoma\u003C/a\u003E dipendono dalla computer vision per interpretare l’ambiente circostante. Videocamere e sensori inviano dati visivi continui a modelli che rilevano pedoni, leggono segnali stradali e riconoscono la segnaletica orizzontale. La stessa tecnologia aiuta le città ad analizzare i flussi di traffico, ottimizzare i semafori e migliorare la sicurezza stradale con monitoraggio in tempo reale.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDiagnostica sanitaria e imaging medico\u003C/h3\u003E\r\n\u003Cp\u003EIn \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/\"\u003Emedicina\u003C/a\u003E, la computer vision supporta i medici identificando pattern che potrebbero sfuggire all’occhio umano. Gli algoritmi possono rilevare tumori nelle radiografie, segmentare tessuti nelle risonanze magnetiche o segnalare anomalie nelle immagini retiniche. Questi strumenti non sostituiscono i clinici, ma offrono second opinion più rapide e coerenti, accelerando diagnosi e trattamento.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalytics nel retail e tracciamento del comportamento dei clienti\u003C/h3\u003E\r\n\u003Cp\u003EI retailer utilizzano la computer vision per capire come le persone si muovono nei punti vendita. Le videocamere tracciano pattern di traffico, interazioni con i prodotti e tempi di permanenza per ottimizzare layout e merchandising. Alcuni sistemi monitorano persino le scorte a scaffale, avvisando il personale quando è necessario rifornire.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERilevamento difetti nel manifatturiero\u003C/h3\u003E\r\n\u003Cp\u003EGli stabilimenti utilizzano sistemi di visione per individuare difetti o deviazioni in tempo reale. Videocamere posizionate lungo le linee di produzione catturano ogni prodotto e gli algoritmi lo confrontano all’istante con la versione ideale. Questo consente di intercettare i difetti in anticipo, ridurre gli scarti e mantenere qualità costante su larga scala.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESistemi di sicurezza e sorveglianza\u003C/h3\u003E\r\n\u003Cp\u003ELa computer vision alimenta le infrastrutture di sicurezza moderne, dal riconoscimento facciale in aeroporto al rilevamento del movimento nelle smart camera. Questi sistemi analizzano continuamente i filmati, distinguono tra movimenti di routine e potenziali minacce e possono attivare alert non appena rilevano attività insolite.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EElaborazione documentale e OCR\u003C/h3\u003E\r\n\u003Cp\u003ELe aziende utilizzano la computer vision per convertire documenti scannerizzati, scontrini e moduli scritti a mano in dati strutturati. Gli strumenti OCR estraggono e organizzano informazioni che possono essere ricercate, validate e inviate direttamente nei workflow aziendali, eliminando la necessità di inserimento manuale dei dati.\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_289473020":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-2f6b3019fb","additionalClasses":"headline-decoration","type":"heading2","lines":["Vantaggi della computer vision"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-624073f08e","text":"\u003Cp\u003EAdottare la computer vision significa lavorare in modo più intelligente e veloce. La tecnologia offre benefici concreti, migliorando accuratezza, velocità ed esperienza utente. Ecco alcuni dei principali vantaggi:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMaggiore automazione ed efficienza\u003C/h3\u003E\n\u003Cp\u003ELa computer vision elimina la necessità che le persone svolgano attività visive ripetitive, liberando tempo per attività a più alto valore. Snellisce le operations, dalle linee di assemblaggio agli hub logistici, aumentando la produttività e riducendo i costi di manodopera.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMaggiore accuratezza nelle attività visive\u003C/h3\u003E\n\u003Cp\u003EI modelli di AI addestrati su enormi dataset possono individuare dettagli sottili che una persona potrebbe non notare, garantendo risultati più coerenti e meno errori. Questa precisione migliora il controllo qualità e aiuta i settori a rispettare standard più stringenti in tema di sicurezza o compliance.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ECapacità decisionale in tempo reale\u003C/h3\u003E\n\u003Cp\u003EElaborando i dati visivi all’istante, la computer vision consente alle organizzazioni di agire mentre gli eventi si verificano. La capacità di rilevare e rispondere in pochi secondi può prevenire incidenti, ridurre i downtime e migliorare la situational awareness.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDeployment scalabile su diverse piattaforme\u003C/h3\u003E\n\u003Cp\u003ELa computer vision può essere eseguita su edge device come smartphone e sensori di fabbrica, oltre che su sistemi di analisi dei dati in cloud. Questa flessibilità consente alle organizzazioni di partire in piccolo e scalare su prodotti, sedi o regioni senza dover riprogettare i sistemi.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERiduzione degli errori umani\u003C/h3\u003E\n\u003Cp\u003EI sistemi di visione automatizzati mantengono prestazioni coerenti, riducono le sviste e aumentano l’affidabilità in contesti in cui l’accuratezza è fondamentale. A differenza delle persone, non si affaticano né perdono concentrazione, quindi i risultati restano stabili indipendentemente dalla durata di funzionamento.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ECustomer e user experience migliori\u003C/h3\u003E\n\u003Cp\u003ELa computer vision consente interazioni più fluide e personalizzate, come lo shopping senza cassa e interfacce adattive. Quando i sistemi riconoscono comportamento e contesto, possono anticipare i bisogni e ridurre gli attriti nelle esperienze quotidiane.\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":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-12141e7245","additionalClasses":"headline-decoration","type":"heading2","lines":["Sfide della computer vision"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-54239e9b5a","text":"\u003Cp\u003ENonostante le sue promesse, la computer vision non è una tecnologia plug-and-play. Per costruire sistemi affidabili occorre superare ostacoli legati a qualità dei dati, prestazioni e integrazione. Ecco alcune delle principali sfide:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVariabilità di qualità delle immagini e illuminazione\u003C/h3\u003E\n\u003Cp\u003EVariazioni di luce, angolazione della fotocamera o risoluzione possono compromettere i risultati di rilevamento. Un modello addestrato su foto chiare e ben illuminate può fallire quando cambiano le condizioni, ad esempio in magazzini poco illuminati o con riflessi all’aperto, rendendo la coerenza degli input una sfida costante.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EElevati requisiti computazionali\u003C/h3\u003E\n\u003Cp\u003EEseguire \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emodelli di deep learning\u003C/a\u003E per analisi in tempo reale richiede hardware potente e può comportare consumi energetici elevati. Addestramento e inferenza su larga scala richiedono spesso GPU o chip specializzati, aumentando costi infrastrutturali e operativi.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDati di addestramento etichettati limitati\u003C/h3\u003E\n\u003Cp\u003ESenza dataset diversi e ben annotati, i modelli faticano a generalizzare e ad adattarsi a nuove condizioni. Raccogliere ed etichettare esempi a sufficienza richiede molto lavoro e le lacune nei dati spesso generano sistemi fragili che performano male fuori dagli scenari ideali.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EBias ed equità nel riconoscimento visivo\u003C/h3\u003E\n\u003Cp\u003EModelli addestrati su dati sbilanciati possono identificare in modo errato o avere prestazioni inferiori per alcuni gruppi demografici. Correggere questi bias richiede ripensare la composizione dei dataset e introdurre processi di test e revisione per individuare le disparità fin dall’inizio.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIntegrazione con sistemi legacy\u003C/h3\u003E\n\u003Cp\u003ELe infrastrutture più datate spesso non offrono prestazioni o compatibilità adeguate ai moderni workload di AI. Collegare nuove piattaforme di computer vision con database esistenti o strumenti operativi può richiedere reengineering dei workflow o l’aggiunta di middleware per colmare i gap.\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":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-1a79265122","additionalClasses":"headline-decoration","type":"heading2","lines":["Il futuro della computer vision"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-9057d3e4d4","text":"\u003Cp\u003ELa computer vision sta evolvendo rapidamente, grazie a nuove tecniche di AI e hardware che la rendono più veloce, intelligente e accessibile. Questi trend emergenti indicano dove si sta dirigendo la tecnologia:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EModellazione spaziale basata su AI e apprendimento multimodale\u003C/h3\u003E\n\u003Cp\u003EI sistemi futuri combineranno dati visivi con altri input sensoriali, come audio, testo e profondità, per comprendere meglio l’ambiente.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVisione in tempo reale su dispositivi edge\u003C/h3\u003E\n\u003Cp\u003EI progressi nelle reti neurali leggere e nei chip efficienti stanno spostando l’analisi dal cloud all’edge.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMappatura 3D e realtà aumentata\u003C/h3\u003E\n\u003Ch3\u003E \u003C/h3\u003E\n\u003Cp\u003ELa computer vision si sta espandendo oltre le immagini bidimensionali verso la comprensione 3D, fondendo mondo fisico e digitale.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EGenerazione di dati sintetici per l’addestramento\u003C/h3\u003E\n\u003Cp\u003EI team di sviluppo utilizzano immagini simulate o generate dall’AI per addestrare i modelli e superare la scarsità di dati.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDemocratizzazione degli strumenti di visione per utenti non tecnici\u003C/h3\u003E\n\u003Cp\u003ELe piattaforme no-code e low-code rendono la computer vision accessibile anche a chi non ha competenze tecniche specifiche, ampliando innovazione e accessibilità.\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":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[]},"container_copy_copy__1190438074":{"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"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[]},"container_copy_2083166428":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"conslusion",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-83c6924237","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-09aa132ed6","text":"\u003Cp\u003ELa computer vision è al centro della rivoluzione dell’AI. Consentendo alle macchine di vedere e interpretare il mondo, trasforma i dati visivi in insight immediati e azionabili. Le stesse tecnologie che alimentano rilevamento degli oggetti, riconoscimento dei pattern e \u003Ca href=\"https://www.snowflake.com/it/product/analytics/\"\u003Eanalisi in tempo reale\u003C/a\u003E stanno cambiando il modo di operare dei settori, rendendo l’automazione più intelligente, la precisione più elevata e la scalabilità più rapida.\u003C/p\u003E\r\n\u003Cp\u003EIn ambiti come sanità, retail, manifatturiero e \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/travel-hospitality/\"\u003Etrasporti\u003C/a\u003E, la computer vision migliora il decision-making e snellisce workflow che prima dipendevano solo dall’intervento umano. Man mano che questi sistemi evolvono, non si limitano ad analizzare ciò che hanno davanti, ma aiutano anche le aziende ad anticipare cosa succederà dopo.\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_copy","text_copy"]},"container_copy_20831":{"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"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-dc8d49e5d9","additionalClasses":"headline-decoration","type":"heading2","lines":["FAQ sulla computer vision"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-b215dc5f95","showDivider":false,"accordionItemsList":[{"title":"Quali sono le 3 R della computer vision?","richText":"\u003Cp\u003ELe 3 R, recognition, reconstruction e re-organization, descrivono come i sistemi di visione danno senso alle immagini. Recognition identifica ciò che è presente. Reconstruction ricostruisce la forma 3D o la disposizione della scena a partire da immagini 2D. Re-organization raggruppa i pixel in parti significative, così che le altre fasi possano operare in modo più veloce e accurato. La maggior parte dei sistemi combina tutte e tre.\u003C/p\u003E\n"},{"title":"Quali strumenti si utilizzano nello sviluppo della computer vision?","richText":"\u003Cp\u003EPer lo sviluppo di soluzioni di computer vision, in genere si utilizzano OpenCV per le operazioni sulle immagini e TensorFlow o PyTorch per addestrare ed eseguire i modelli. Il deployment avviene su servizi cloud come Azure o AWS, oppure su dispositivi edge quando la latenza è critica. Piattaforme data cloud come Snowflake aiutano a gestire dati di addestramento, feature e pipeline che alimentano questi modelli.\u003C/p\u003E\n"},{"title":"Quali sono gli algoritmi di computer vision più comuni?","richText":"\u003Cp\u003ELe reti neurali convoluzionali (CNN) alimentano attività come riconoscimento e rilevamento degli oggetti. Metodi classici come le Haar cascade compaiono ancora in rilevatori facciali leggeri, mentre l’optical flow traccia il movimento tra fotogrammi video. Molti sistemi in produzione combinano questi approcci per bilanciare velocità e accuratezza.\u003C/p\u003E\n"},{"title":"Qual è la differenza tra elaborazione delle immagini e computer vision?","richText":"\u003Cp\u003EL’elaborazione delle immagini migliora un’immagine, ad esempio rimuovendo rumore o regolando il contrasto. La computer vision interpreta l’immagine: identifica oggetti, segmenta regioni e attiva azioni in base a ciò che “vede”.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"},"container":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customers",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2":{"id":"title-v2-f320b8864d","additionalClasses":"headline-decoration","type":"heading2","lines":["Clienti che utilizzano Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-8054684921","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-ab58f0ee8e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2":{"id":"card-v2-4b507b63ec","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003ECon Snowflake come base per l’Agentic AI, Simon Data aiuta gli esperti di marketing ad aumentare i ricavi offrendo personalizzazione contestuale su vasta scala, il tutto senza spostare i dati o compromettere la governance.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["Simon Data evolve il marketing con agenti AI componibili basati su 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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi i dettagli"},"image":{"id":"image","height":"720","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fc3568f8-1374-44a7-b560-0b1ea179bf05/simon-data-customer-card%25402x.jpg?preferwebp=true&quality=85","lazyEnabled":true,"alt":"merkle 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