What is Computer Vision? The Complete Technology Guide for 2022

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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:

  1. What is Computer Vision?
  2. How does Computer Vision work?
  3. The history of Computer Vision
  4. Current Trends of Computer Vision
  5. Computer Vision Applications
  6. Computer Vision Research Fields
  7. Computer Vision Ideas and Projects
  8. 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. Hence, computer vision aims to make computers see and understand visual data input from cameras or sensors.

 

Computer Vision example of object detection
Computer Vision example of Object Detection with the YOLO algorithm
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. 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.

 

Computer Vision system for automated mask detection
Computer Vision system for automated mask detection
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.

The latest deep learning models achieve above human-level accuracy and performance in real-world image recognition tasks such as facial recognition, object detection, and image classification.

Computer vision applications are used in a wide range of industries, ranging from security and medical imaging to manufacturing, automotive, agriculture, construction, transportation, smart city, and many more. As the technology advances and becomes more flexible and scalable, more use cases become possible.

According to a report (2021) 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

To build and deploy such applications effectively, we’ve built a no-code computer vision platform Viso Suite that helps enterprises and startups to deliver AI vision much faster and more agile.

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.

 

Computer Vision in Manufacturing
Computer Vision for Equipment Detection
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).

Convolutional Neural Networks Concept
Concept of Convolutional Neural Networks (CNN)
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 software and AI algorithms are based on deep learning, where computers can take in an image and process that visual information. Information derived from computational vision depends on the application; it can be the type of objects detected or the number of specific objects and their size and position within the image.

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.

 

Computer vision in animal farming
Example of a computer vision system in agriculture for smart animal farming
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

In recent years, new deep learning technologies achieved great breakthroughs in the field of computer vision, especially in image recognition and object detection.

  • 1960 – The beginnings. Computer vision came to light in the 1960s, where 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 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.

 

Video analytics with deep learning for vehicle detection
Video analytics with deep learning for vehicle detection

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.

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:

 

Computer Vision used in Livestock Farming
Computer Vision used in Livestock Farming for animal monitoring

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.
1.) 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.

2.) 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.

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.

3.) 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.

Existing architectures for OCR extractions include EasyOCR, Python-tesseract, or Keras-OCR. These machine learning software tools are popularly used for Number Plate Recognition as an example.

4.) 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.

5.) 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.

6.) 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.

Computer Vision based human pose estimation
Computer Vision based human pose estimation

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:

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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.

 

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