All about Computer Vision: Definition, applications, history & future (2023)

Computer vision explained in more detail

Computer vision is an artificial intelligence-based technology that assists computers in associating meaning with digital images, videos, or any other form of visual input.

Computer vision helps computers visually interpret and understand the real world. It is analogous to how human eyes gather visual data and help our brains perceive real-world objects. But unlike how humans learn to see the world around them intuitively, computers need to be taught or programmed to understand visual data.

This is achieved with the help of artificial intelligence techniques such as deep learning models to analyze images to identify real-world objects with as much accuracy as possible. The input for computer vision could be from any digital image or video format.

Computer vision is also called “machine vision” and is often used alongside AI to denote intelligent computing systems.

Several automation, surveillance, and security applications are now dependent on computer vision for advanced AI operations such as facial recognition, pattern recognition, image classification, and more.

The application and presence of computer vision products have become almost ubiquitous, given the huge number of images available online. Some factors that continue to contribute to further developments in computer vision include:

  • Smartphones and their camera apps allow for a huge influx of digital photos and videos
  • Access to advanced computing power at a lower cost
  • Ease of access to computer vision hardware and software. Most smartphones have computer vision embedded in camera apps, facial IDs, and several image editing apps.
  • Continued research and development of advanced algorithms and deep learning AI models help take advantage of available hardware and software capabilities to aid in continued advancement in the computer vision field.

Understanding Computer Vision in the Real World

With the increasing power of computers and also the much larger sample size of images currently available, computer vision has flourished. Used in tools like facial recognition, it can be used to identify specific people and while early tests had an accuracy of 50%, with the amount of data now available, that number has been far surpassed.

Today, computer vision is used in many different applications and areas. We can apply it in our online searches for example to find a specific breed of dog or it can enhance details on images, helping us zoom in and understand what would have once only been pixels.

Of course, the most common use of computer vision is facial recognition. At its most benign it can be used as a tool to unlock smartphones and automatically detect people in your uploaded social media snaps. Facial recognition does have some privacy implications however, and these should not be discounted.

Order visual datasets for machine learning such as photos and videos from clickworker that are tailored to train your AI system.

Computer Vision in the World of AI

Computer vision is all about patterns and pattern recognition. By providing the underlying software with visual data, it learns to understand and extrapolate. It is critical that the data used is correct and labeled appropriately. If for example millions of cat pictures were provided to the algorithm but they were actually picturing dogs, the system would not be able to correctly identify a cat in the future.

Computer vision can be paired with AI in many areas and one of the most promising is healthcare. Doctors and hospitals are now using the technology to find different types of cancer at earlier stages, helping to save lives.
Another use case for the partnership of computer vision and AI is the autonomous vehicle. Self-driving cars need to see what is all around and understand the difference between a parked car and one that is moving. By using computer vision, autonomous vehicles are able to analyze the information from cameras all over the vehicle and make appropriate decisions.

All about Computer Vision: Definition, applications, history & future (1)

Origins and history

The field of computer vision started with the development of neural networks in the 1950s. The earliest computer vision applications could detect edges from an image and use that information to classify objects into shape categories like circles, squares, and so on. Computer vision continues to be a popular field of interest among AI developers, and it has since grown to be more advanced and highly accurate.

In the 1970s, computer vision products were first used as commercial products. These applications allowed for the digitization of handwritten or typed text and made it possible for visually challenged individuals to interpret written text without assistance. Since then, computer vision has been a major driver behind several accessibility features in computer applications. With the advent of the internet in the 1990s, the field of computer vision experienced rapid growth as the internet provided huge volumes of image data.

The accuracy rate of computer vision systems for object identification and classification has steadily increased, from 50% in the previous decade to nearly 99% today.

Computer vision is used across various industries, making it a part of daily life. The market for computer vision is estimated to hit 20.88 billion USD by 2030.

YouTube Video on How Computer Vision Works – By Google Cloud Tech

How does computer vision work?

Computer vision is designed to mirror human visual perception. While humans have a biological system involving the eyes, optic nerves, and the brain’s visual cortex, computers use digital cameras, visual data, and advanced algorithms to understand visual information.

To achieve a good level of visual acuity, the computing system needs to be trained with many images. For instance, for a computer to identify an object as being circular, it needs to be previously fed what a circle looks like. The neural networks behind computer vision technology use hundreds of thousands of images to learn about specific objects and classify them into several categories. And then, when an input image is provided for analysis, the computer vision system compares it to the stored characteristics and tries to identify the given image.

For instance, to identify a cat, computer vision needs to be trained with several pictures of cats. The learning model will try to correlate the various features that make up a cat’s image and then use them to identify whether an input image is of a cat or not.

In essence, these are the steps involved in computer vision technology.

  • Step 1: Model training

Deep learning models and AI technology are used to create a computer vision application. The modes are usually trained first with training data consisting of processed image data.

  • Step 2: Input image acquisition

Even in large quantities, an image or a set of images can be gathered as digital image formats, real-time video, or 3D technology for analysis.

  • Step 3: Image processing

The input image is processed using an AI model previously trained to identify the characteristic patterns in the image.

  • Step 4: Interpreting the image
  • The object(s) in the image is(are) understood or interpreted as the application requires. Most commonly, the output of this step is that the objects in the image are identified or classified.
  • Identification means that the image is coined to be a particular object. For instance, in a photo displaying an ongoing football game, computer vision can identify each individual player in the photo.
  • Classification refers to understanding the common attributes of an object and assigning them to a specific category of objects. For instance, in a photo showing a furnished living room, computer vision can classify certain objects as chairs, certain objects as wall hangings, and so on.

Based on the model used, a computer vision system can be designed to learn continuously, improve, and have learning transferred and retained as and when required.

Here are some of the major techniques used by computer vision systems:

  • Image Segmentation: The input can be segmented into multiple regions and individually analyzed. For instance, topographical images can be segmented based on certain surface characteristics, and each segment can be further examined in more detail.
  • Object detection: Object detection is used to identify a specific object in a given image.
  • Facial recognition: Facial recognition is an advanced form of object detection where each individual is identified using their unique facial characteristics.
  • Edge detection: Edge detection is used to identify edges in an image. For instance, in a living room photo, this method can outline the room’s shape and identify all the sharp edges of objects in the room.
  • Pattern detection: Pattern detection with computer vision identifies similar patterns across different images and thus groups objects into various categories. Recognizing repeated visual characteristics can be used for image detection and classification.
  • Feature matching: It is the process by which the computer vision system tries to compare and match different images or visual characteristics.

A computer vision system could be classified based on the techniques it uses and the applications that it is used for.

The technology behind computer vision

Deep learning is a machine learning model that trains the computer to learn about images. It allows the computer to self-learn images and the characteristic patterns across the images and find correlations among different images. It is the basis for computer vision applications. Specifically, convolutional neural networks (CNN) are the predominant technology used in computer vision systems. It was first developed in 1988 by Yann Lecun and was used to identify zip codes and digits on images.

CNN, also called ConvNet, is a type of neural network that uses a layered architecture to process image data.

Depending on the data training model used, the computer vision system can be programmed to carry out many tasks related to visual information interpretation. Anyone can develop models according to their requirements and needs.

The advantages of computer vision

  • Automation

Computer vision allows for the gathering and analyzing of visual information without the need for manual intervention. This helps automate several manual tasks like surveillance, unmanned vehicle operations, etc.

  • Faster analysis

Computer vision can be faster and allow for more comprehensive investigation during image analysis. For instance, using computer vision in medical imaging can make diagnoses much faster, and the data collected can be stored accurately.

  • Improved accuracy

Computer vision, in recent times, is more accurate and allows for faster processing than human visual capabilities. This helps for deeper analysis of images, videos, and so on. For instance, analyzing gameplay in sports is done more accurately with the help of computer vision.

  • Detect duplicates and defects

Minute details and visual information can be easily processed with the help of computer vision.

  • Disaster recovery

Computer vision can help identify, detect, and enable timely action in various situations of damaged machinery, pipe leaks, and similar incidents. It can be used to operate unmanned vehicles and drones, thus allowing us to reach otherwise unreachable spots.

  • Improved security

Computer vision allows for facial recognition and authentication and is thus used in many security systems worldwide.

Industries and applications of computer vision

Computer vision is employed by various industries and domains, including retail, manufacturing, government, healthcare, defense and security, and more. Here are some industry-specific applications of computer vision:

  • Manufacturing

Computer vision can identify defects that are otherwise difficult to spot with the human eye.

  • Sports

Computer vision allows for a deeper analysis of the gameplay and player performance. It helps identify every movement of the player and thus can be a huge help in analyzing game performance and can also be used to boost audience engagement.

  • Forensic analysis of images

Computer vision, with its faster processing and attention to detail, can be used as a means to detect forgeries in art and counterfeit currency bills. It can also be used for deeper image investigations.

  • Retail

Computer vision is also used to automate billing, invoicing, and checkouts. Image recognition systems have long been used to scan QR codes, barcodes, and OCR documents in the retail industry. The retail industry also makes use of facial recognition to authorize transactions.

  • Healthcare

Computer vision can assist and speed up medical diagnosis to a great extent. It is specifically found to help identify areas of concern in patients suffering from liver and brain cancer.

  • Agriculture

Computer vision can be used in agriculture to automate certain tasks and monitor crops. It can also identify the onset of plant diseases early on and thus help improve the yield.

  • Insurance

Computer vision helps identify the authenticity of auto damage from any image evidence presented. It can also verify any visual evidence presented for insurance claims.

Challenges and concerns with computer vision

While computer vision may have improved phenomenally over the past few decades, it still faces certain challenges when implemented successfully. Here are the major reasons why a computer vision system could fail:

  • Lack of adequate hardware

The accuracy of computer vision output depends on the quality of the input images. Poor cameras, inadequate sensors, and improper installation of hardware can be detrimental to the accuracy of computer vision systems.

  • Inadequate training data

Lack of accurate and quality training data can be a huge issue with computer vision systems. Much of the output and efficiency of the learning models used in computer vision systems depend on the quality of the training data used. Lack of data or poor quality data will result in a poor computer vision system. For instance, one of the biggest challenges faced in medical imaging and diagnosis is gathering quality training data with proper annotations. As medical data is sensitive and protected by regulations, accessing the huge amounts of data required for accurate medical-related computer vision systems can be challenging.

  • Weak learning models and algorithm design

Each learning model used in a computer vision system should be designed to meet the particular needs of the application where it is to be used. A model may fail when it tries to achieve impossible goals and assumes unrealistic computing power. A good model must meet the business objectives, have realistic computing power requirements, be scalable, and have an acceptable range of accuracy in its results.

  • Time constraints

Training a computer vision model takes time, followed by a thorough testing process. The input data used for training must be collected and handled without bias. It needs to be properly cleaned, labeled, and pre-processed before being used for training. Failing to allocate the required resources for data preparation and testing might affect the model’s accuracy.

Trends and future development

Some of the cool ongoing trends in the computer vision space are:

  • Autonomous cars and unmanned drone control
  • Edge computing applications use computer vision to allow the visually challenged to navigate the world around them.
  • Healthcare applications such as the InnerEye software aid in the more accurate detection of anomalies and conditions in medical images.
  • Agri-based applications to predict crop yield and detect any abnormalities early on.
  • Digitization of banking is expected to rely more on computer vision techniques such as OCR for faster data collection, facial recognition, and image detection for processes like KYC (know your customer) and AML(anti-money laundering), and more.

Computer vision has lots of room to grow and is continuously evolving. It is used in various industries and domains, from manufacturing to retail and even social media content creation.

Further developments in the field point to advancements in 3D space, augmented reality, and virtual reality applications.

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