Computer vision: Everything you need to know

  • Computer vision integrates image processing, pattern recognition, and AI to enable machines to analyse visual data, simulating and augmenting human intelligence for complex problem-solving.
  • Applications span medicine, public safety, drones, autonomous driving, and industry, aiding in diagnostics, security, navigation, quality control, and robotics.
  • Challenges include data limitations, resource-intensive training, hardware demands, and the inherent complexity of interpreting varied visual scenarios.

Computer vision is the process of extracting symbolic or numerical information from images or videos, analysing and computing this information for tasks such as object recognition, detection, and tracking. Simply put, computer vision enables computers to see and understand images like humans.

Introduction to computer vision

Computer vision (CV) is an emerging interdisciplinary field that involves image processing, image analysis, pattern recognition, and artificial intelligence. It is characterised by being fast, real-time, cost-effective, consistent, objective, and non-destructive.

Computer vision is the science of studying how to enable machines to “see.” It can simulate, extend, and augment human intelligence, thereby helping humans solve large-scale complex problems. Therefore, computer vision is one of the major application areas of artificial intelligence.

The basic principle of computer vision technology is to use image sensors to obtain image signals of the target object, which are then transmitted to a dedicated image processing system. This system converts image information such as pixel distribution, colour, and brightness into digital signals and performs various operations and processing on these signals. The system extracts the target’s feature information for analysis and understanding, ultimately achieving recognition, detection, and control of the target.

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How does computer vision work?

The computer vision system comprises two main components: a sensory device, such as a camera, and an interpreting device, like a computer. The sensory device captures visual data from the environment, while the interpreting device processes this data to derive meaningful information.

Computer vision algorithms operate on the premise that “our brains rely on patterns to decode individual objects.” Similar to how our brains interpret visual data by recognising patterns in shapes, colours, and textures, computer vision algorithms analyse images by identifying patterns in the pixels that compose the image. These patterns help in identifying and classifying various objects within the image.

To analyse an image, a computer vision algorithm first transforms the image into numerical data that the computer can process. This process typically involves dividing the image into a grid of small units called pixels and representing each pixel with numerical values that describe its colour and brightness. These values form a digital representation of the image, enabling computer analysis.

After converting the image into numerical data, the computer vision algorithm begins its analysis. This typically involves applying machine learning and artificial intelligence techniques to recognise patterns in the data and make decisions based on those patterns. For instance, an algorithm might analyse pixel values to detect object edges or recognise specific patterns or textures characteristic of certain types of objects.

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Applications of computer vision

Medical applications

Currently, image processing technologies used in medicine include compression, storage, transmission, and automatic/assisted classification interpretation. These technologies can also be used for doctors’ auxiliary training. Related work includes classification, interpretation, and rapid 3D structure reconstruction.

Public safety applications

The public safety field is a significant application scenario for computer vision technology, especially facial recognition. This technology is essential for building a three-dimensional, modern social security and prevention system, with important applications in current security measures.

Drone and autonomous driving applications

The rise of the drone and autonomous driving industries has made computer vision in these fields a research hotspot. For example, in drones, applications range from simple aerial photography to complex tasks like rescue and disaster relief and aerial refuelling, all requiring high-precision visual signals to ensure decision-making and action reliability. A critical subsystem in the core navigation system of drones is the vision system.

Industrial applications

Computer vision also has significant applications in the industrial sector. It is a key technology in industrial robotics, enabling functions such as product appearance inspection, quality control, product classification, and component assembly when combined with mechanical devices.

The applications of computer vision are extensive. Beyond the fields mentioned above, it has numerous applications in other industries (such as agriculture and services), providing increasing convenience to human life.

Challenges of computer vision

Computer vision is a complex field with numerous challenges and difficulties, including:

Data limitations

Computer vision requires large datasets to train and test algorithms. This can be problematic when data is scarce or sensitive, making it unsuitable for cloud processing. Additionally, scaling up data processing is often expensive and can be limited by hardware and other resources.

Learning rate

Training computer vision algorithms demands significant time and resources. Although error rates have decreased over time, errors still occur, and it takes time to train computers to recognise and classify objects and patterns in images. This process typically involves providing sets of labelled images, comparing them to the predicted output, and adjusting the algorithm to correct any errors.

Hardware requirements

Computer vision algorithms are computationally intensive, requiring fast processing speeds and optimised memory architecture for efficient memory access. Properly configured hardware systems and software algorithms are essential to ensure that image-processing applications run smoothly and efficiently.

Inherent complexity in the visual world

In the real world, subjects can appear from various angles and under different lighting conditions, creating an infinite number of possible scenes for a vision system to interpret. This inherent complexity makes it challenging to develop a general-purpose “seeing machine” capable of handling all potential visual scenarios.


Crystal Feng

Crystal Feng is an intern news reporter at Blue Tech Wave dedicated in tech trends. She is studying Chinese-English translation at Beijing International Studies University. Send tips to

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