{"id":9355,"date":"2024-08-21T16:39:35","date_gmt":"2024-08-21T14:39:35","guid":{"rendered":"https:\/\/www.neurally.it\/computer-vision"},"modified":"2025-02-28T12:40:20","modified_gmt":"2025-02-28T11:40:20","slug":"computer-vision","status":"publish","type":"page","link":"https:\/\/www.neurally.it\/en\/computer-vision","title":{"rendered":"What is Computer vision?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"9355\" class=\"elementor elementor-9355 elementor-3945\" data-elementor-post-type=\"page\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a8a2ae8 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-parent\" data-id=\"a8a2ae8\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3c5cb2f elementor-widget elementor-widget-eael-fancy-text\" data-id=\"3c5cb2f\" data-element_type=\"widget\" data-widget_type=\"eael-fancy-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n\t<div  class=\"eael-fancy-text-container style-1\" data-fancy-text-id=\"3c5cb2f\" data-fancy-text=\"|Computer Vision\" data-fancy-text-transition-type=\"typing\" data-fancy-text-speed=\"50\" data-fancy-text-delay=\"3000\" data-fancy-text-cursor=\"yes\" data-fancy-text-loop=\"\" >\n\t\t\n\t\t\n\t\t\t\t\t<span id=\"eael-fancy-text-3c5cb2f\" class=\"eael-fancy-text-strings solid-color\">\n\t\t\t\t<noscript>\n\t\t\t\t\tComputer Vision\t\t\t\t<\/noscript>\n\t\t\t<\/span>\n\t\t\n\t\t\t<\/div><!-- close .eael-fancy-text-container -->\n\n\t<div class=\"clearfix\"><\/div>\n\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bc577bc e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-parent\" data-id=\"bc577bc\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-1182a40 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"1182a40\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-38efbf2 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"38efbf2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What is?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5793aaf elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"5793aaf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tComputer vision is a field of artificial intelligence that <font class=\"bold-stronger\">enables computer systems to extract meaningful information from images, videos and other visual input.<\/font> By leveraging cameras, algorithms, and powerful computing systems, it allows machines to perceive, analyze, and interpret visual data, executing tasks rapidly and at scale.\nMuch like human vision, computer vision can identify objects, determine their positions, recognize movements, and detect anomalies.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-214a210 elementor-align-center elementor-widget-mobile__width-initial elementor-widget elementor-widget-button\" data-id=\"214a210\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-shrink\" href=\"https:\/\/www.neurally.it\/en\/area\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Neurally's case study<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-dbb9790 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-parent\" data-id=\"dbb9790\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-4fbdf3b e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"4fbdf3b\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d908aba elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"d908aba\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The story<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7b92af7 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"7b92af7\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-559f816 elementor-widget elementor-widget-heading\" data-id=\"559f816\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">1959<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e71d36f e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"e71d36f\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8e89365 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"8e89365\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The first significant experiments were conducted by neurophysiologists who showed a series of images to a cat, attempting to link the brain\u2019s response to visual stimuli. They discovered that the cat primarily reacted to sharp edges and lines, suggesting that image processing begins with simple shapes.<br>Around the same time, the first computer-based image scanning technology was developed, enabling systems to digitize and capture images. This breakthrough laid the foundation for modern computer vision, allowing machines to process and analyze visual data in ways that mimic early stages of human perception.  <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4890596 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"4890596\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-96e918f elementor-widget elementor-widget-heading\" data-id=\"96e918f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">1963<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-14379c4 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"14379c4\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-10198b4 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"10198b4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Computers became able to transform two-dimensional images into three-dimensional shapes. During these years, AI emerged as an academic research field, and the first attempts to apply it to human vision began to take shape. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-30d7c9a e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"30d7c9a\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-453db07 elementor-widget elementor-widget-heading\" data-id=\"453db07\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">1974<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-814d571 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"814d571\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cdc388b elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"cdc388b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Optical Character Recognition (OCR) technology was introduced, allowing computers to recognize printed text in various fonts. At the same time, Intelligent Character Recognition (ICR) was developed to decipher handwritten text using neural networks.<br>These advancements paved the way for many common applications, including document processing, license plate recognition, mobile payments, and automated translation.  <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0072de9 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"0072de9\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5102c85 elementor-widget elementor-widget-heading\" data-id=\"5102c85\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">1982<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d34ea90 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"d34ea90\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6eaae57 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"6eaae57\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Neuroscientist David Marr demonstrated that vision operates hierarchically and introduced algorithms that enabled machines to detect edges, corners, curves, and basic shapes. Around the same time, computer scientist Kunihiko Fukushima developed a network of cells capable of recognizing patterns, called the Neocognitron, which incorporated convolutional layers into a neural network. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-49e2cc8 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"49e2cc8\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b750493 elementor-widget elementor-widget-heading\" data-id=\"b750493\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">2000<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-693c9cc e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"693c9cc\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e1770de elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"e1770de\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Research began focusing on object recognition, and by 2001, the first real-time facial recognition applications emerged. Additionally, the standardization of visual datasets\u2014through labeling and annotation\u2014became widely established, providing a crucial foundation for training and evaluating computer vision models. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e243981 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"e243981\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-08d1efc elementor-widget elementor-widget-heading\" data-id=\"08d1efc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">2010<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9a45320 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"9a45320\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ba907d6 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"ba907d6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The ImageNet dataset was released, containing millions of labeled images across thousands of object categories. This dataset became a cornerstone for the development of convolutional neural networks (CNNs) and modern deep learning models, significantly advancing the field of computer vision.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-51c63ed e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"51c63ed\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d312300 elementor-widget elementor-widget-heading\" data-id=\"d312300\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">2012<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e73ced6 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"e73ced6\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-167a7ee elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"167a7ee\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A team from the University of Toronto introduced a CNN in an image recognition program called AlexNet, which significantly reduced error rates in image classification. This breakthrough led to a dramatic drop in error rates, bringing them down to just a few percentage points, and marked a pivotal moment in the evolution of computer vision. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8671f39 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-parent\" data-id=\"8671f39\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-bad3a63 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"bad3a63\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ab18992 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"ab18992\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How does computer vision work?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-63d89ee elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"63d89ee\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To recognize and interpret images, computer vision relies on vast amounts of data. Through repeated analysis, the system learns to identify key image features such as shapes, colors, and patterns. Technologies like deep learning and CNN play a crucial role in this process.<br>Deep learning enables machine learning models to improve autonomously by analyzing large sets of visual data. CNN break images down into pixels, label this information, and apply convolutions to make accurate predictions.<br>This iterative process allows machines to interpret images in a way that resembles human vision, continuously improving the accuracy of their predictions. Depending on the techniques used, the type of image, and the specific task, computer vision algorithms can perform various analyses and validations on an image.  <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-40a932b e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"40a932b\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-47ffd56 elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"47ffd56\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"74\" height=\"74\" viewBox=\"0 0 74 74\" fill=\"none\"><path d=\"M70 14.6195C70 11.5378 68.7746 8.58236 66.5933 6.40328C64.412 4.2242 61.4536 3 58.3688 3H17.2123C14.1275 3 11.169 4.2242 8.98776 6.40328C6.80649 8.58236 5.58106 11.5378 5.58106 14.6195V30.7331C7.19164 29.4041 9.00373 28.3397 10.9493 27.5798V14.6195C10.9493 11.1658 13.7551 8.36286 17.2123 8.36286H58.3688C61.826 8.36286 64.6318 11.1658 64.6318 14.6195V55.7348C64.627 56.4856 64.5029 57.1947 64.2596 57.862L43.4236 37.476L42.9655 37.0613C41.869 36.1427 40.5473 35.5326 39.1364 35.2938C37.7255 35.055 36.2764 35.1962 34.9382 35.7027C35.8651 37.2222 36.588 38.8811 37.0783 40.6365C37.5313 40.5123 38.0091 40.5089 38.4638 40.6269C38.9184 40.7448 39.3342 40.9799 39.6694 41.3087L60.4445 61.6339C59.7756 61.8652 59.0731 61.9848 58.3652 61.9879H41.2763L43.1087 63.822C44.1036 64.8123 44.6941 66.0601 44.8802 67.3507H58.3652C59.8927 67.3512 61.4052 67.0511 62.8166 66.4676C64.2279 65.8841 65.5104 65.0286 66.5908 63.95C67.6712 62.8713 68.5283 61.5907 69.1133 60.1811C69.6982 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29.8311C14.6744 29.6798 10.7301 31.0479 7.65749 33.6643C4.58489 36.2808 2.60811 39.9547 2.11877 43.9584C1.62943 47.962 2.66323 52.0031 5.01536 55.2813C7.36749 58.5595 10.8664 60.8355 14.8188 61.6584C18.7713 62.4814 22.889 61.7913 26.3562 59.7247L36.7885 70.1466C37.0342 70.41 37.3305 70.6213 37.6598 70.7679C37.989 70.9144 38.3444 70.9932 38.7048 70.9996C39.0652 71.0059 39.4232 70.9397 39.7575 70.8048C40.0917 70.67 40.3953 70.4693 40.6502 70.2146C40.905 69.96 41.106 69.6567 41.241 69.3228C41.376 68.9889 41.4423 68.6313 41.4359 68.2713C41.4295 67.9112 41.3507 67.5562 41.204 67.2272C41.0573 66.8983 40.8457 66.6023 40.582 66.3568L30.4539 56.2353ZM18.107 56.6286C15.2595 56.6286 12.5286 55.4986 10.5151 53.4871C8.50164 51.4756 7.37048 48.7475 7.37048 45.9029C7.37048 43.0582 8.50164 40.3301 10.5151 38.3186C12.5286 36.3072 15.2595 35.1771 18.107 35.1771C20.9545 35.1771 23.6853 36.3072 25.6988 38.3186C27.7123 40.3301 28.8435 43.0582 28.8435 45.9029C28.8435 48.7475 27.7123 51.4756 25.6988 53.4871C23.6853 55.4986 20.9545 56.6286 18.107 56.6286Z\" fill=\"#E24494\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-528daaa elementor-widget elementor-widget-text-editor\" data-id=\"528daaa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Image classification<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e65b47 elementor-widget elementor-widget-text-editor\" data-id=\"5e65b47\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Image analysis and label assignment<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-464733d e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"464733d\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-000882f elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"000882f\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"70\" height=\"70\" viewBox=\"0 0 70 70\" fill=\"none\"><g clip-path=\"url(#clip0_1447_394)\"><path d=\"M4.375 26.25V39.375H17.5V43.75H0V26.25H4.375ZM43.75 26.25V43.75H26.25V39.375H39.375V26.25H43.75ZM17.5 0V4.375H4.375V17.5H0V0H17.5ZM43.75 0V17.5H39.375V4.375H26.25V0H43.75Z\" fill=\"#E24494\"><\/path><path d=\"M35 52.5C37.3206 52.5 39.5462 53.4219 41.1872 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class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Object Detection<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c3f5202 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"c3f5202\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Identification of objects within an image<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d72aaec e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"d72aaec\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-80f42c4 elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"80f42c4\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"81\" height=\"60\" viewBox=\"0 0 81 60\" fill=\"none\"><path d=\"M81 0V60H0V0H81ZM75.9375 5H5.0625V25H15.1875V35H5.0625V55H45.5625V45H55.6875V55H65.8125V45H75.9375V5ZM25.3125 25H15.1875V15H25.3125V25ZM25.3125 35V25H35.4375V35H25.3125ZM45.5625 35V45H35.4375V35H45.5625ZM65.8125 35V45H55.6875V35H65.8125ZM65.8125 25H55.6875V15H65.8125V25Z\" fill=\"#E24494\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c64f8b elementor-widget elementor-widget-text-editor\" data-id=\"0c64f8b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Image Segmentation<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20e36f5 elementor-widget elementor-widget-text-editor\" data-id=\"20e36f5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Dividing the image into distinct segments to facilitate recognition<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-db82097 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"db82097\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a8a3a64 elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"a8a3a64\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"79\" height=\"79\" viewBox=\"0 0 79 79\" fill=\"none\"><path d=\"M56.7812 34.5625C59.5082 34.5625 61.7188 32.3519 61.7188 29.625C61.7188 26.8981 59.5082 24.6875 56.7812 24.6875C54.0543 24.6875 51.8438 26.8981 51.8438 29.625C51.8438 32.3519 54.0543 34.5625 56.7812 34.5625Z\" fill=\"#E24494\"><\/path><path d=\"M69.125 12.3435H40.0924C38.2821 10.0672 35.9914 8.21881 33.384 6.93039C30.7765 5.64197 27.9166 4.94533 25.0088 4.89026C22.1009 4.83519 19.2167 5.42303 16.5623 6.61179C13.9079 7.80056 11.5489 9.5609 9.65365 11.767C7.75844 13.9732 6.37387 16.5707 5.59887 19.3739C4.82386 22.1772 4.67754 25.117 5.17038 27.9834C5.66321 30.8497 6.78303 33.5719 8.44981 35.9553C10.1166 38.3387 12.2892 40.3246 14.8125 41.771V66.656C14.8125 67.9655 15.3327 69.2214 16.2586 70.1474C17.1846 71.0733 18.4404 71.5935 19.75 71.5935H69.125C70.4345 71.5935 71.6903 71.0733 72.6163 70.1474C73.5423 69.2214 74.0625 67.9655 74.0625 66.656V17.281C74.0625 15.9715 73.5423 14.7157 72.6163 13.7897C71.6903 12.8637 70.4345 12.3435 69.125 12.3435ZM9.87495 24.6873C9.88071 21.4314 10.9591 18.2681 12.9432 15.6866C14.9274 13.1052 17.7069 11.2494 20.8517 10.4062C23.9965 9.56311 27.3316 9.77965 30.341 11.0223C33.3504 12.265 35.8665 14.4647 37.5003 17.281H19.75V22.2185H39.2778C39.4108 23.0351 39.4851 23.8601 39.4999 24.6873C39.4851 25.5145 39.4108 26.3395 39.2778 27.156H24.6874V32.0935H37.5003C35.8665 34.9099 33.3504 37.1095 30.341 38.3522C27.3316 39.5949 23.9965 39.8114 20.8517 38.9683C17.7069 38.1252 14.9274 36.2694 12.9432 33.6879C10.9591 31.1065 9.88071 27.9432 9.87495 24.6873ZM69.125 66.656H19.75L32.0937 54.3123L36.019 58.2376C36.9441 59.1572 38.1955 59.6734 39.4999 59.6734C40.8044 59.6734 42.0558 59.1572 42.9809 58.2376L56.7812 44.4373L69.125 56.781V66.656ZM69.125 49.7945L60.2621 40.9317C59.337 40.012 58.0856 39.4959 56.7812 39.4959C55.4768 39.4959 54.2254 40.012 53.3003 40.9317L39.4999 54.732L35.5746 50.8067C34.6495 49.887 33.3981 49.3709 32.0937 49.3709C30.7893 49.3709 29.5379 49.887 28.6128 50.8067L19.75 59.6695V43.7954C21.3631 44.2117 23.0215 44.4273 24.6874 44.4373C29.9255 44.4373 34.949 42.3565 38.6528 38.6526C42.3567 34.9488 44.4375 29.9253 44.4375 24.6873C44.4389 22.1472 43.9441 19.6314 42.9809 17.281H69.125V49.7945Z\" fill=\"#E24494\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e2f7a12 elementor-widget elementor-widget-text-editor\" data-id=\"e2f7a12\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Action Recognition<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-39e52ce elementor-widget elementor-widget-text-editor\" data-id=\"39e52ce\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Identification of objects and their links in space and time<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e327f63 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-parent\" data-id=\"e327f63\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-4c4a735 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"4c4a735\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6920bb8 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"6920bb8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Where can we apply Computer Vision?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fe7a0bb elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"fe7a0bb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tComputer vision is applied across various industries, <span class=\"bold-stronger\">from manifacturing to heathcare, from transportation to entertainment. <\/span>For example, in autonomous vehicles, this technology plays a crucial role in recognizing traffic signs, pedestrians, and other vehicles, ensuring safe and independent driving.\nAnother example is the use of computer vision in security systems, where it analyzes video feeds to detect suspicious activity.<br><br><span class=\"bold-stronger\">\nHere is how we have applied Computer Vision in Neurally<\/span><br>Based on the research and experience of the Neurally team, we developed a software called AREA. Using Computer Vision, AREA detects objects in one or multiple sections of a live or recorded video. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80cdfe6 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"80cdfe6\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-shrink\" href=\"https:\/\/area-oi.it\/\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover Area<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Computer Vision What is? Computer vision is a field of artificial intelligence that enables computer systems to extract meaningful information from images, videos and other visual input. By leveraging cameras, algorithms, and powerful computing systems, it allows machines to perceive, analyze, and interpret visual data, executing tasks rapidly and at scale. Much like human vision, computer vision can identify objects, determine their positions, recognize movements, and detect anomalies. Neurally&#8217;s case study The story 1959 The first significant experiments were conducted by neurophysiologists who showed a series of images to a cat, attempting to link the brain\u2019s response to visual stimuli. They discovered that the cat primarily reacted to sharp edges and lines, suggesting that image processing begins with simple shapes.Around the same time, the first computer-based image scanning technology was developed, enabling systems to digitize and capture images. This breakthrough laid the foundation for modern computer vision, allowing machines to process and analyze visual data in ways that mimic early stages of human perception. 1963 Computers became able to transform two-dimensional images into three-dimensional shapes. During these years, AI emerged as an academic research field, and the first attempts to apply it to human vision began to take shape. 1974 Optical Character Recognition (OCR) technology was introduced, allowing computers to recognize printed text in various fonts. At the same time, Intelligent Character Recognition (ICR) was developed to decipher handwritten text using neural networks.These advancements paved the way for many common applications, including document processing, license plate recognition, mobile payments, and automated translation. 1982 Neuroscientist David Marr demonstrated that vision operates hierarchically and introduced algorithms that enabled machines to detect edges, corners, curves, and basic shapes. Around the same time, computer scientist Kunihiko Fukushima developed a network of cells capable of recognizing patterns, called the Neocognitron, which incorporated convolutional layers into a neural network. 2000 Research began focusing on object recognition, and by 2001, the first real-time facial recognition applications emerged. Additionally, the standardization of visual datasets\u2014through labeling and annotation\u2014became widely established, providing a crucial foundation for training and evaluating computer vision models. 2010 The ImageNet dataset was released, containing millions of labeled images across thousands of object categories. This dataset became a cornerstone for the development of convolutional neural networks (CNNs) and modern deep learning models, significantly advancing the field of computer vision. 2012 A team from the University of Toronto introduced a CNN in an image recognition program called AlexNet, which significantly reduced error rates in image classification. This breakthrough led to a dramatic drop in error rates, bringing them down to just a few percentage points, and marked a pivotal moment in the evolution of computer vision. How does computer vision work? To recognize and interpret images, computer vision relies on vast amounts of data. Through repeated analysis, the system learns to identify key image features such as shapes, colors, and patterns. Technologies like deep learning and CNN play a crucial role in this process.Deep learning enables machine learning models to improve autonomously by analyzing large sets of visual data. CNN break images down into pixels, label this information, and apply convolutions to make accurate predictions.This iterative process allows machines to interpret images in a way that resembles human vision, continuously improving the accuracy of their predictions. Depending on the techniques used, the type of image, and the specific task, computer vision algorithms can perform various analyses and validations on an image. Image classification Image analysis and label assignment Object Detection Identification of objects within an image Image Segmentation Dividing the image into distinct segments to facilitate recognition Action Recognition Identification of objects and their links in space and time Where can we apply Computer Vision? Computer vision is applied across various industries, from manifacturing to heathcare, from transportation to entertainment. For example, in autonomous vehicles, this technology plays a crucial role in recognizing traffic signs, pedestrians, and other vehicles, ensuring safe and independent driving. Another example is the use of computer vision in security systems, where it analyzes video feeds to detect suspicious activity. Here is how we have applied Computer Vision in NeurallyBased on the research and experience of the Neurally team, we developed a software called AREA. Using Computer Vision, AREA detects objects in one or multiple sections of a live or recorded video. Discover Area<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-9355","page","type-page","status-publish","hentry"],"rankMath":{"parentDomain":"www.neurally.it","noFollowDomains":[],"noFollowExcludeDomains":[],"noFollowExternalLinks":false,"featuredImageNotice":"The featured image should be at least 200 by 200 pixels to be picked up by Facebook and other social media sites.","pluginReviewed":false,"postSettings":{"linkSuggestions":true,"useFocusKeyword":false},"frontEndScore":false,"postName":"computer-vision","permalinkFormat":"https:\/\/www.neurally.it\/en\/%pagename%","showLockModifiedDate":true,"assessor":{"focusKeywordLink":"https:\/\/www.neurally.it\/wp-admin\/edit.php?focus_keyword=%focus_keyword%&post_type=%post_type%","hasTOCPlugin":true,"primaryTaxonomy":false,"serpData":{"title":"What is Computer Vision? 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