Computer Vision (CV)

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What Is Computer Vision (CV)?

Computer vision technology is a part of artificial intelligence (AI) that helps computers understand and act on information from images, videos, and other visual data, similar to how people see and understand what they look at.

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CV systems use deep learning, especially a type called convolutional neural networks (CNNs), to recognize and analyze objects, shapes, and details in an image. For videos, recurrent neural networks (RNNs) help computers understand how images in a sequence are connected, allowing them to track changes from one frame to the next.

Knowing what computer vision is used for helps us understand how it powers important tools like facial recognition for security and navigation for self-driving cars.

What Is Computer Vision (CV)?

Key Takeaways

  • Computer vision is a part of AI that helps computers understand images and videos, similar to how humans see. It uses CNNs for images and RNNs for videos.
  • CV systems learn by training on large sets of data to recognize patterns. CNNs help analyze details in images, while RNNs track movement in videos, enabling tasks like object detection and classification.
  • CV is a specific part of AI that focuses only on visual data, while AI covers a wide range of tasks. Both rely on deep learning models.
  • CV is used for real-time security monitoring, medical diagnostics, crop monitoring in farming, and navigation in self-driving cars, among other examples. It powers AI tools that generate visual output, such as AI image or video generators.
  • Improvements in cloud computing and the Internet of Things (IoT) will expand CV’s role in areas like smart cities and autonomous vehicles.

History of Computer Vision Development

The origins of computer vision technology date back to the 1950s, with studies on how animals process visual information.

In the 1960s, computers could start scanning and digitizing images, and by the 1970s, they could recognize printed text through optical character recognition (OCR) and even handwritten text with intelligent character recognition (ICR).

In the 1980s, improvements in algorithms and neural networks helped computers recognize patterns more effectively.

Major advancements in computer vision development came in the 2000s with the ImageNet dataset in 2010 and AlexNet in 2012, which greatly improved accuracy in image recognition, moving CV closer to real-world use.

How Does Computer Vision Work?

CV relies on large amounts of data, algorithms, and special neural networks to understand and interpret visual information.

CV systems are trained on millions of labeled images to help them build models that recognize patterns and objects in new images. Then, CV uses CNNs to process visual data at the pixel level and RNNs to understand how one pixel relates to another.

Key tasks in CV include segmentation (dividing images into parts), object detection (finding specific objects), and classification (grouping objects by type). These steps enable CV to identify and track objects quickly and accurately.

Computer Vision Technology vs. Artificial Intelligence

CV is a specific area within AI focused on interpreting and understanding visual data, like images and videos.

In comparison, AI covers a much broader range of functions, such as:

  • Language processing
  • Decision-making
  • Problem-solving

Computer vision is unique because it is dedicated to visual interpretation and pattern recognition, which makes it different from general AI. However, CV and AI share a reliance on deep learning models.

This shared use of deep learning makes CV part of AI, but it remains distinct with its focus on understanding visual information.

Computer Vision Uses

To understand what computer vision is used for, here is a list of examples:

Biometric access management
CV plays an important role in both facial and iris recognition.
Industrial robots and self-driving cars
CV allows robots and autonomous vehicles to avoid collisions and navigate safely.
Digital diagnostics
CV can be used in tandem with other types of artificial intelligence programming to automate the analysis of X-rays and MRIs.
Augmented reality (AR)
CV allows mixed reality programming to know where a virtual object should be placed.

If you’re interested in tools that use AI for video, check out the best AI video generators to explore the best options for automated video creation.

Computer Vision Examples

Computer vision and self-driving cars

Here are some ways that computer vision is used in self-driving cars:

  • Allow self-driving cars to identify and distinguish objects such as traffic lights, pedestrians, and road signs.
  • Detect and track the edges of the road.
  • Plan a path through traffic by predicting the motion of other objects in the environment.
Computer vision and facial recognition

Here are some ways that computer vision is used in facial recognition:

  • Identify specific facial features and use this information to determine whether a face is present.
  • Extract a set of unique facial features, such as the distance between the eyes or the shape of the nose, to create a template that represents a specific individual’s face.
  • Match the template with a single image in the database.
Computer vision and medical imaging

Here are some ways that computer vision is used in medical imaging:

  • Segment medical images into different regions of interest, such as organs or tissues.
  • Remove noise and enhance image contrast to make it easier for doctors to identify abnormalities.
  • Support computer-aided diagnosis systems that analyze medical images and provide physicians with diagnostic support.

Computer Vision Pros and Cons

Pros

  • Automates complex tasks, speeding up processes in industries like manufacturing
  • Reduces human error, especially in repetitive tasks
  • Assists in real-time monitoring for security and health diagnostics
  • Offers applications like real-time translation, making information accessible across languages

Cons

  • CV models need large datasets for training, which can be expensive and complicated to gather
  • Processing visual data requires a lot of power and memory, often needing specialized hardware
  • Variations in lighting, angles, and partially hidden objects challenge CV systems in accurate image interpretation
  • In critical areas like healthcare and self-driving cars, even small mistakes can have serious consequences

Computer Vision Future

Computer vision’s future will be driven by advancements in cloud computing and the Internet of Things.

As CV development aligns with cloud technology, it will allow companies to work with larger datasets and enable faster, real-time processing, making it more accessible and efficient. Improvements in computational power and new data training techniques are also making CV systems quicker and more accurate. Ethics in AI is also becoming increasingly important, pushing for responsible use of CV and respect for privacy.

The future of what computer vision is used for includes significant impacts on industries like autonomous vehicles and smart cities, where it will support safe navigation, infrastructure checks, and urban planning, helping CV advance alongside AI.

The Bottom Line

CV is changing industries by helping computers understand and use visual data, which makes tasks like security, medical diagnostics, and self-driving navigation faster and more accurate.

With improvements in deep learning, CV systems can now handle complex visual tasks, from real-time monitoring to detailed diagnostics. The combination of CV with cloud computing and IoT is expanding its reach. As ethics in AI become more important, CV will play a major role in smart cities and self-driving cars.

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Maria Webb
Technology Journalist
Maria Webb
Technology Journalist

Maria is Techopedia's technology journalist with over five years of experience with a deep interest in AI and machine learning. She excels in data-driven journalism, making complex topics both accessible and engaging for her audience. Her work is also prominently featured on Eurostat. She holds a Bachelor of Arts Honors in English and a Master of Science in Strategic Management and Digital Marketing from the University of Malta. Maria's background includes journalism for Newsbook.com.mt, covering a range of topics from local events to international tech trends.