Computer vision is one of the most fascinating and complex artificial intelligence techniques, inspired from the outset by the idea of seeing the world as the human eye would, neurally connected to the biological brain.
\nIf AI represents the brain, artificial vision can be associated with the activity of the eye and its fundamental functions, which enable us to see and understand real-world phenomena. In other words, computer vision allows artificial systems to observe and interpret the context being analysed, drawing inspiration from what a human would do in the same circumstances.
\nComputer vision is a complex discipline that involves interdisciplinary studies and research, including within the field of artificial intelligence itself, where machine learning, deep learning, convolutional neural networks (CNNs), and many other techniques come into play.
\nThrough Moxoff\u2019s experience, we explore what computer vision is, how it works, and the advantages that can currently be gained from its informed implementation in business processes.<\/p>\n
According to the definition provided by Gartner, computer vision<\/strong> is \u201ca set of technologies that involves capturing, processing, and analysing real-world images and videos in order to extract relevant and contextual information from the physical world<\/em>\u201d. The origins of artificial vision can be traced back to the 1950s, when some neurophysiologists began presenting images to animals in order to understand their brain reactions. As previously mentioned, artificial vision makes use of digital data and artificial intelligence techniques, including machine learning and deep learning, to replicate the workings of human vision. The algorithms used in computer vision applications are trained using large datasets of visual information, learning how to classify, identify, and track objects within a given context.<\/p>\n The process of image analysis makes use of pattern recognition<\/strong> techniques. The acquired image is interpreted by comparing it with reference models known beforehand, for example, detecting abnormal conditions that may correspond to a quality defect in a product, an attempted fraud, or a possible early sign of pathology in radiological examinations. The fundamental functions of a computer vision system are classification, identification and object tracking.<\/p>\n Within its interdisciplinary context, it is important to highlight that modern computer vision adopts a different approach compared to traditional statistical methods for image analysis, utilising deep learning techniques<\/strong> based on machine learning through neural networks. These are capable of extracting much more knowledge from images than traditional systems. One of the main advantages of machine learning lies in its ability to continually enhance the value of image analysis, using the outputs as part of the input for the next cycle, thereby improving the model\u2019s predictive accuracy.<\/p>\n When implemented correctly and thoughtfully, computer vision enables companies to achieve significant advantages in every respect.<\/p>\n In synergy with other AI techniques, computer vision enables the automation of activities traditionally carried out manually, such as sorting and packaging in warehouses and distribution centres. This allows for faster processing times and reduces the need for low value-added human labour, which is replaced by robotic systems.<\/p>\n Computer vision is able to automate many processes, making them more economically sustainable and increasing productivity levels as a result. Consider, for example, the ability to recognise text, which underpins document management or real-time translation applications, or the ability to recognise objects and people within social media contexts.<\/p>\n
\nComputer vision uses devices, data, and algorithms to analyse what the human eye would perceive, but in a much faster and more accurate way, seeking to provide a similar interpretation. For these reasons, computer vision is used to support human vision, especially in highly automated contexts where it is necessary to see where the biological eye cannot reach. For example, analysing processes, inspecting products or checking the functionality of production assets, even going so far as to detect anomalies that are imperceptible to humans.
\nComputer vision boasts applications in numerous fields and underpins functions that are now part of our daily lives, such as text recognition (OCR), facial recognition, or augmented reality itself.<\/p>\nThe evolution of Computer Vision<\/span><\/h2>\n
\nIn the 1960s, the first digital scanning technologies were developed and research in the field of artificial intelligence began to address the problem of vision. During the following decade, the first OCR (optical character recognition) applications<\/strong> were created to recognise and digitise handwritten text, using neural networks.
\nIn the 1980s, neuroscientist David Marr translated the hierarchical workings of vision into algorithms capable of automatically detecting the edges of an object and its basic shapes. In those years, the Neocognitron was developed, the first convolutional neural network (CNN), which paved the way for the next generation of computer vision.
\nAfter the so-called 'AI winter', the 2000s saw the emergence of applications we still use today, such as facial recognition, along with the definition of standard methods for visual data labelling.
\nIn the 2010s, convolutional neural networks became increasingly efficient and accurate in their predictions, reducing the risk of error in image recognition to percentages acceptable even in production contexts.<\/p>\nHow Computer Vision works<\/span><\/h2>\n
Image analysis<\/h3>\n
\nThanks to machine learning, pattern recognition systems process increasing volumes of images over time, progressively improving their level of accuracy and meeting application requirements more reliably.<\/p>\nFundamental functions<\/h3>\n
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Artificial Intelligence<\/h3>\n
The advantages of Computer Vision<\/span><\/h2>\n
Improvement of operational efficiency<\/h3>\n
Cost reduction and productivity increase<\/h3>\n
Greater safety and product quality<\/h3>\n