What is computer vision, and how does it work? This article provides a complete guide to Computer Vision, one of the key fields of artificial intelligence (AI). AI vision enables computer systems to analyze digital images, videos, and other visual input with computational methods to derive information that can be used to take action or make decisions based on it.
In this article, you will learn everything you need to know about the technology of Computer Vision in 2022:
- What is Computer Vision?
- How does Computer Vision work?
- The history of Computer Vision
- Current Trends of Computer Vision
- Computer Vision Applications
- Start a Computer Vision Project
What Is Computer Vision?
Computer Vision (CV) is a field of Artificial Intelligence (AI) that deals with computational methods to help computers understand and interpret the content of digital images and videos. Hence, computer vision aims to make computers see and understand visual data input from cameras or sensors.
Definition of Computer Vision
Computer vision tasks seek to enable computer systems to automatically see, identify and understand the visual world, simulating human vision using computational methods.
Computer Vision vs. Human Vision
Computer vision aims to artificially imitate human vision by enabling computers to perceive visual stimuli meaningfully. It is therefore also called machine perception, or machine vision. While the problem of “vision” is trivially solved by humans (even by children), computational vision remains one of the most challenging fields in computer science, especially due to the enormous complexity of the varying physical world.
Human sight is based on a lifetime of learning with context to train how to identify specific objects or recognize human faces or individuals in visual scenes. Hence, modern artificial vision technology uses machine learning and deep learning methods to train machines on how to recognize objects, faces, or people in visual scenes.
As a result, computer vision systems use image processing algorithms to allow computers to find, classify, and analyze objects and their surroundings from data provided by a camera.
What Is the Value of Computer Vision?
Computer vision systems are trained to inspect products, watch infrastructure, or a production asset to analyze thousands of products or processes in real-time, noticing defects or issues. Due to its speed, objectivity, continuity, accuracy, and scalability, it can quickly surpass human capabilities.
Computer vision applications are used in a wide range of industries, ranging from security and medical imaging to manufacturing, automotive, agriculture, construction, smart city, transportation, and many more. As the technology advances and becomes more flexible and scalable, more use cases become possible.
According to a 2021 report of Verified Market Research, the AI in Computer Vision Market size was valued at USD 7 Billion in 2020 and is projected to reach USD 144 Billion by 2028, growing at a CAGR of 45% from 2021 to 2028.
Computer Vision Platform to Build Applications
The computer vision platform Viso Suite enables leading organizations worldwide to develop, scale and operate their AI vision applications. The Viso platform provides no-code and low-code capabilities to dramatically accelerate the complex development and maintenance of deep learning applications across industries.
Viso covers the entire lifecycle of computer vision, from image annotation and model training to visual development, one-click deployment, and scaling to hundreds of cameras. The platform offers zero-trust security and powers privacy-preserving AI, critical for business applications.
The modular and extensible architecture of Viso Suite helps companies to connect computer vision with PowerBI, Tableau, or external databases.
How Does Computer Vision Work?
Generally, computer vision works in three basic steps:
- Step #1: Acquiring the image/video from a camera,
- Step #2: Processing the image, and
- Step #3: Understanding the image.
A Practical Example of Computer Vision
Computer vision machine learning requires a massive amount of data to train a deep learning algorithm that can accurately recognize images. For example, to train a computer to recognize a helmet, it needs to be fed large quantities of helmet images with people wearing helmets in different scenes to learn the characteristics of a helmet.
Next, the trained algorithm can be applied to newly generated images, for example, videos of surveillance cameras, to recognize a helmet. This is, for example, used in computer vision applications for equipment inspection to reduce accidents in construction or manufacturing.
How Computer Vision Technology Works
To train an algorithm for computer vision, state-of-the-art technologies leverage deep learning, a subset of machine learning. Many high-performing methods in modern computer vision software are based on a convolutional neural network (CNN).
Such layered neural networks are used to enable a computer to learn about the context of visual data from images. If enough data is available, the computer learns how to tell one image from another. As image data is fed through the model, the computer applies a CNN to “look” at the data.
The CNN helps a machine learning/deep learning model to understand images by breaking them down into pixels that were given labels to train specific features, so-called image annotation. The AI model uses the labels to perform convolutions and make predictions about what it is “seeing” and checks the accuracy of the predictions iteratively until the predictions meet the expectation (start to come true).
Computational Vision Inspired by the Human Brain
Hence, computer vision works by recognizing images or “seeing” images similar to humans, using learned features with a confidence score. Therefore, neural networks essentially simulate human decision-making, and deep learning trains the machine to do what the human brain does naturally. The characteristic layered structure of deep neural networks is the foundation of artificial neural networks. Each layer adds to the knowledge of the previous layer.
Human-level Performance of Computer Vision AI
Deep learning tasks are computationally heavy and expensive, depending on significant computing resources, and require massive datasets to train models on. Compared to traditional image processing, deep learning algorithms enable machines to learn by themselves, without a developer programming it to recognize an image based on pre-determined features. As a result, deep learning methods achieve very high accuracy.
Today, deep learning enables machines to achieve human-level performance in image recognition tasks. For example, in deep face recognition, AI models achieve a detection accuracy (e.g., Google FaceNet achieved 99.63%) that is higher than the accuracy humans can achieve (97.53%). Computational vision with deep learning has also achieved human performance in classifying skin cancer with a level of competence comparable to dermatologist experts.
What Is a Computer Vision System
Modern computer vision systems combine image processing with machine learning and deep learning techniques. Hence, developers combine different software (etc. OpenCV or OpenVINO) and AI algorithms to create a multi-step process, a computer vision pipeline.
The organization and setup of a computer vision system vary based on the application and use case. However, all computer vision systems contain the same typical functions:
- Step #1: Image acquisition. The digital image of a camera or image sensor provides the image data or video. Technically, any 2D or 3D camera or sensor can be used to provide image frames.
- Step #2: Pre-processing. The raw image input of cameras needs to be preprocessed to optimize the performance of the subsequent computer vision tasks. Pre-processing includes noise reduction, contrast enhancement, re-scaling, or image cropping.
- Step #3: Computer vision algorithm. The image processing algorithm, most popularly a deep learning model, performs object detection, image segmentation, and classification on every image or video frame.
- Step #4: Automation logic. The AI algorithm output information needs to be processed with conditional rules based on the use case. This part performs automation based on information gained from the computer vision task. For example, pass or fail for automatic inspection applications, match or no-match in recognition systems, flag for human review in insurance, security, military, or medical recognition applications.
The Best Computer Vision Deep Learning Models Today
In computer vision, specifically, object detection, there are single-stage and multi-stage algorithm families.
- Single-stage algorithms aim for real-time processing and the highest computational efficiency. The most popular algorithms include SSD, RetinaNet, YOLOv3, YOLOv4, and YOLOR.
- Multi-stage algorithms perform multiple steps and achieve the highest accuracy but are rather heavy and resource-intensive. The widely used multi-stage algorithms include Recurrent Convolutional Neural Networks (R-CNN) such as Mask-RCNN, Fast RCNN, and Faster RCNN.
History of Computer Vision Technology
- 1960 – The beginnings. Computer vision came to light in the 1960s, when computer scientists tried to mimic human eyesight using computing mechanics. Although computer vision research has spent several decades teaching machines how to see, the most advanced machine at that time could only perceive common objects and struggled at recognizing multiple natural objects with infinite shape variations.
- 2014 – The era of Deep Learning. Researchers achieved great breakthroughs by training computers with the 15 million images of the largest image classification dataset, ImageNet using deep learning technology. In computer vision challenges and benchmarks, deep learning demonstrated overwhelming superiority over traditional computer vision algorithms that treat objects as a collection of shape and color features.
- 2016 – Near real-time Deep Learning. Deep learning, a particular class of machine learning algorithms, simplifies the process of feature extraction and description through a multi-layer convolutional neural network (CNN). Powered by massive data from ImageNet, modern central processing units (CPU), and graphics processing units (GPU), deep neural networks bring an unprecedented development of computer vision and achieve state-of-the-art performance. Especially, the development of single-stage object detectors made deep learning AI vision much faster and more efficient.
- 2020 – Deep Learning deployment and Edge AI. Today, CNN has become the de-facto standard computation framework in computer vision. Numbers of deeper and more complex networks were developed to make CNNs deliver near-human accuracy in many computer vision applications. Optimized, lightweight AI models make it possible to perform computer vision on inexpensive hardware and mobile devices. Edge AI hardware such as deep learning hardware accelerators enables highly efficient Edge Inference for computer vision.
Current Trends in Computer Vision and State-of-the-Art
The latest trends combine Edge Computing with on-device Machine Learning; a method also called Edge AI. Moving AI processing from the cloud to edge devices makes it possible to run computer vision machine learning everywhere and build scalable computer vision applications.
We see a trend in falling computer vision costs, driven by higher computational efficiency, decreasing hardware costs, and new technologies (model compression, low-code/no-code, automation). As a result, more and more computer vision applications become possible and economically feasible – further accelerating the adoption of computer vision.
The most important Computer Vision trends right now are:
- Trend #1: Real-Time Video Analytics
- Trend #2: AI Model Optimization and Deployment
- Trend #3: Hardware AI Accelerators
- Trend #4: Edge Computer Vision
- Trend #5: Real-world computer vision applications
Real-Time Video Analytics
Traditional machine vision systems commonly depend on special cameras and highly standardized settings. In contrast, modern deep learning algorithms are much more robust, easy to re-use and re-train and allow the development of computer vision applications across industries.
Modern deep learning computer vision methods can analyze video streams of common, inexpensive surveillance cameras or webcams to perform state-of-the-art AI video analytics.
AI Model Optimization and Deployment
After a decade of deep learning training, aiming to improve the accuracy and performance of algorithms, we now enter the era of deep learning deployment. AI model optimization and new architectures made it possible to drastically reduce the size of machine learning models while increasing computational efficiency. This makes it possible to run deep learning computer vision without depending on expensive and energy-consuming AI hardware and GPUs in datacenters.
Hardware AI Accelerators
Meanwhile, we face a boom in high-performance deep learning chips that are increasingly energy-efficient and run on small form-factor devices and edge computers. Current popular deep learning AI hardware for computer vision includes edge computing devices such as embedded computers and SoC devices, including the Nvidia Jetson Tx2, Intel NUC, or Google Coral.
AI accelerators for neural networks can be attached to embedded computing systems. The most popular hardware neural network AI accelerators for computer vision include the Intel Myriad X VPU, Google Coral, or Nvidia NVDLA.
Edge Computer Vision
Traditionally, computer vision and AI in general, were pure cloud solutions due to the unlimited availability of computing resources and easy scalability to increase resources. Web or cloud computer vision solutions require uploading all images or photos to the cloud, either directly or using a computer vision API such as AWS Rekognition, Google Vision API, Microsoft image recognition API (Azure Cognitive Services), or Clarifai API.
In mission-critical use cases, data offloading with a centralized cloud design is usually not possible because of technical (latency, bandwidth, connectivity, redundancy) or privacy reasons (sensitive data, legality, security), or because it is too expensive (real-time, large-scale, high-resolution, bottlenecks cause cost spikes). Hence, edge computing concepts are used to overcome the limits of the cloud; the cloud is extended to multiple connected edge devices.
Edge AI, also called Edge Intelligence or on-device ML, uses edge computing and the internet of things (IoT) to move machine learning from the cloud to edge devices in close proximity to the data source such as cameras. With the massive, still exponentially growing amount of data generated at the edge, AI is required to analyze and understand data in real-time without compromising the privacy and security of visual data.
Real-world Computer Vision Applications
Hence, computer vision at the edge leverages the advantages of the cloud and the edge to make AI vision technology scalable, flexible, and therefore suitable for real-world applications. On-device computer vision does not require data offloading and inefficient centralized image processing in the cloud.
Also, Edge Computer Vision does not fully depend on connectivity, requires much lower bandwidth and reduced latency, especially important in video analytics. Therefore, Edge Computer Vision allows the development of private, robust, secure, and mission-critical real-world applications.
Since Edge AI involves the internet of things (AIoT) to manage distributed devices, the superior performance of Edge Computer Vision comes at the cost of increased technical complexity.
Computer Vision Applications and Use Cases
Companies are rapidly introducing computer vision technology across industries to solve automation problems with computers that can see. Visual AI technology is quickly advancing, making it possible to innovate and implement new computer vision ideas and projects:
- Manufacturing: Industrial computer vision is used in manufacturing industries for automated product inspection, object counting, process automation, and to increase workforce safety with PPE detection and mask detection.
- Healthcare: Among applications of computer vision in healthcare, a prominent example is automated human fall detection to create a fall risk score and trigger alerts.
- Security: In video surveillance and security, person detection is performed for intelligent perimeter monitoring. Another popular use case is deep face detection and facial recognition with above human-level accuracy.
- Agriculture: Use cases of computational vision in agriculture and farming include automated animal monitoring to detect animal welfare, and early detect diseases and anomalies.
- Smart Cities: Computer vision is a key strategy in smart cities for crowd analysis, weapon detection, traffic analysis, vehicle counting, and infrastructure inspection.
- Retail: For example, the video of surveillance cameras in retail stores can be used to track the movement patterns of customers, for performing people counting or footfall analysis to identify bottlenecks, customer attention, and waiting times.
- Insurance: Computer vision in Insurance leverages AI vision for automated risk management and assessment, claims management, visual inspection, and forward-looking analytics.
- Logistics: AI vision in Logistics applies deep learning to implement AI-triggered automation, save costs by reducing human errors, predictive maintenance, and accelerating operations throughout the supply chain.
- Pharmaceutical: Computer vision in pharmaceutical industries is used for packaging and blister detection, capsule recognition, and visual inspection for equipment cleaning.
Computer Vision Research
Key fields of computer vision research involve the fundamental visual perception tasks:
- Object recognition: Determine whether image data contains one or multiple specified or learned objects or object classes.
- Facial recognition: Recognize an individual instance of a human face by matching it with database entries.
- Object detection: Analyze image data for a specific condition, and localize instances of semantic objects of given classes.
- Pose estimation: Estimate the orientation and position of a specific object relative to the camera.
- Optical character recognition: Identify characters in images (numberplates, handwriting, etc.), usually combined with encoding the text in a useful format.
- Scene understanding: Parse an image into meaningful segments for analysis.
- Motion analysis: Tracking the movement of interest points (keypoints) or objects (vehicles, objects, humans, etc.) in an image sequence or video.
What is Image Classification?
Image classification forms the fundamental building block of Computer Vision. Computer Vision engineers often start with training a Neural Network to identify different objects in an image (Object Detection). Training a network to identify the difference between two objects in an image implies building a binary classification model. On the other hand, if it is more than two objects in an image, then it is a multi-classification problem.
It is important to note that to successfully build any image classification model that can scale or be used in production, the model has to learn from enough data. Transfer learning is an image classification technique that leverages existing architectures that have been trained to learn enough from huge data samples. The learned feature or task is then utilized to identify similar samples. Another term for this is knowledge transfer.
With the idea of transfer learning, Computer Vision engineers have built scalable solutions in the business world with a small amount of data. Existing architectures for image classification include ResNet-50, ResNet-100, ImageNet, AlexNet, VggNet and more.
What is Image Processing?
Image processing is a key aspect of AI vision systems since it involves transforming images in order to extract certain information or optimize it for subsequent tasks in a computer vision system. Basic image processing techniques include smoothing, sharpening, contrasting, de-noising or colorization.
The de facto standard tool for image processing is called OpenCV, initially developed by Intel and currently used by Google, Toyota, IBM, Facebook, and so on.
Image preprocessing is used to remove unnecessary information and help the AI model learn the images’ features effectively. The goal is to improve the image features by eliminating unwanted falsification and achieve better classification performances.
A common application of image processing is super-resolution. This technique typically transforms low-resolution images into high-resolution images. Super-resolution is a major challenge most computer vision engineers encounter because they often get the model information from low-quality images.
What is Optical Character Recognition?
Optical character recognition or optical character reader (OCR) is a computer vision technique that converts any kind of written or printed text from an image into a machine-readable format.
What is Image Segmentation?
While image classification aims to identify the labels of different objects in an image, instance segmentation tries to find the exact boundary of the objects in the image.
There are two types of Image Segmentation techniques: Instance segmentation and semantic segmentation. Instance segmentation differs from semantic segmentation in the sense that it returns a unique label to every instance of a particular object in the image.
What is Object Detection with AI?
Object detection is a key computer vision technology that focuses on detecting an object in an image and then tracking the object through a series of frames.
Object Detection is often applied to video streams, whereby the user is trying to track multiple objects simultaneously with unique identities. Popular architectures of object detection include the AI vision algorithms YOLO, R-CNN, or MobileNet.
What is Pose Estimation?
Pose Estimation makes computers understand the human pose. Popular architectures around Pose Estimation include OpenPose, PoseNet, DensePose, or MeTRAbs. These have been applied to solve real-world problems like, for example, crime detection via poses or ergonomic assessments to improve organizational health.
Read More Expert Articles
Computer vision is an imperative aspect of companies using AI today. If you enjoyed this article, we suggest you read more about the topic:
- The 11 Most Popular Computer Vision Tools in 2022
- Learn about reasons why computer vision projects fail – and how to succeed.
- View an extensive list of real-world AI vision applications across industries.
- Read an easy-to-understand guide about what is machine learning and deep learning?
Implement Computer Vision Projects
Industry leaders worldwide deliver their computer vision projects with Viso Suite, the most powerful end-to-end computer vision platform to build, deploy and monitor computer vision applications.
- Viso Suite is the leading enterprise no-code computer vision platform that provides visual programming to develop computer vision applications 10x faster.
- One platform provides automated tools and robust infrastructure to build scalable Edge Computer Vision systems.
- The best AI models and AI hardware are supported out of the box, skip writing code from scratch.
Explore the Viso Platform and reach out to our team of AI vision experts to discuss your computer vision ideas, see the key platform features and learn how to get started fast.