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AI Computer Vision
Written by: Exquitech Group
AI computer vision is a recent technology that is enabling computers to accurately detect visual inputs, such as images and video. Many industries, including the automobile industry and medical industry, are taking advantage of intelligent technology.
But what is computer vision exactly and how does it work?
In this article, we'll explore how deep learning algorithms make computer vision work. We'll discuss the three-step process of how computer vision works and point out some practical applications of the technology. We'll also discuss how Microsoft Azure uses computer vision and how Exquitech can help you implement Azure’s computer vision technology into your business.
Computer vision is an aspect of artificial intelligence (AI) that trains computers and systems to recognise and interpret digital images, videos, and visual inputs using machine learning models and neural networks. By analysing visual data, computer vision can provide automated insights, recommendations, and actions based on detected objects, patterns, or anomalies.
Computer vision in AI aims to give computer systems the ability to understand visual data, similar to human vision. It involves training artificial intelligence models, particularly deep neural networks, on vast datasets of labelled images and videos. Computer vision works in a three-step process:
The first step is perception, where the AI system acquires visual inputs like images or video frames through cameras or existing datasets. It can then apply image processing techniques like noise reduction, colour correction, and image segmentation to analyse image data, identify relevant features from visual images, and prepare the raw visual data.
Next is the cognition phase, where deep learning models like convolutional neural networks (CNNs) analyse the visual inputs. These models have been trained on millions of sample images to learn how to automatically detect, locate, and classify various objects and images present in the data.
The CNNs break down the images through multiple filters to detect complex patterns and visual features like edges, shapes, and textures. This hierarchical learning allows the models to make sense of the visual world, similar to how the human brain processes visual information.
Based on what the model has recognised, it can generate relevant outputs like classification labels or textual descriptions. This visual understanding can then be used to make intelligent actions.
For example, a self-driving car can identify pedestrians and avoid collisions, or medical professionals can use medical imaging AI to analyse scans and provide a diagnosis.
The power of computer vision AI lies in its ability to automatically learn visual representations directly from data, continuously improving its performance through techniques like transfer learning.
As AI models become more advanced, their capabilities continue to approach and even surpass human capabilities for tasks like object recognition.
Computer vision is an intriguing field of computer science, as it has a wide range of applications. A few of the many computer vision applications include:
AI-powered object identification and recognition systems can accurately identify and locate multiple objects within digital images or video frames.
This capability is crucial for applications like self-driving cars. Through real-time object detection, AI can perceive the car's surroundings, detect obstacles, read traffic signs, and navigate safely. By continuously processing visual inputs from multiple cameras and sensors, self-driving cars can make real-time decisions and adjust their behaviour accordingly, ensuring a safe and efficient journey.
Object detection and recognition are also used in intelligent surveillance systems and automated inventory management.
Facial recognition is a popular application of computer vision AI and has many applications, including for security and surveillance purposes. By detecting and matching faces from digital images against databases, these systems can identify individuals, verify identities, and track people across different locations. Facial recognition is also used in social media platforms for automatic tagging and photo organisation.
Many healthcare organisations are using digital solutions to increase efficiency. In the healthcare domain, vision systems play a vital role in analysing medical images such as X-rays, CT scans, and MRI scans.
AI algorithms can help medical professionals in disease diagnosis, treatment planning, and surgical guidance by accurately detecting abnormalities in patients.
Medical image analysis may be one of the most promising applications of computer vision, as it allows medical professionals to give fast and accurate diagnoses to patients, potentially saving lives.
Optical Character Recognition (OCR) is a computer vision application that extracts printed or handwritten text from a digital image or document. This image recognition technology enables the conversion of physical documents into editable and searchable digital formats, streamlining data entry processes and facilitating text processing and analysis.
In industrial settings, computer vision is useful in quality control and production line monitoring. Computer vision systems can automatically inspect products for defects, ensuring consistent quality standards and reducing the need for manual inspection. Additionally, these systems can track and monitor manufacturing processes, optimising operations and identifying potential issues before they escalate.
There are also many computer vision applications in retail and inventory management. Retail businesses can use a computer vision system for inventory management, customer tracking, and even cashier-less checkout systems.
By analysing visual data from cameras and sensors, these systems can monitor stock levels, optimise product placement, and provide insights into customer behaviour and preferences.
As AI models and deep learning techniques continue to advance, the way computer vision is used is rapidly expanding, revolutionising industries and automating tasks that previously required humans to visually inspect products and analyse images.
Microsoft Azure is Microsoft's cloud computing platform. The platform provides a comprehensive suite of computer vision services that can greatly benefit businesses across various industries. Through computer vision systems, Azure lets organisations extract valuable insights from images and videos using pre-built AI models or by training custom models tailored to their specific needs.
Through various computer vision techniques, Azure Vision can perform tasks such as object detection, image classification, optical character recognition (OCR), and facial recognition. This can be incredibly useful for computer vision applications like product categorisation, document digitisation, and security and surveillance systems.
Additionally, Azure Custom Vision enables businesses to build and deploy their own computer vision models without the need for extensive machine learning knowledge.
For businesses looking to harness the power of machine vision in Azure, partnering with Exquitech can be a game-changer.
As a leading digital transformation partner, Exquitech offers comprehensive support in implementing Microsoft Azure solutions. Exquitech's team can help you seamlessly integrate computer vision capabilities into your existing workflows, enabling you to unlock new insights, automate processes, and drive innovation.
Contact Exquitech today to successfully implement Azure Vision into your business.
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