Are AI and Computer Vision Independent Domains or Interconnected Entities?
Computer vision is a fascinating subfield of artificial intelligence that equips machines and systems with the ability to derive valuable insights from digital images, videos, and other visual inputs and then take appropriate actions based on that information. While AI empowers computers to think, computer vision enables them to see, perceive, and comprehend.
Compared to computer vision, human vision has the advantage of being around much longer. Throughout our lifetimes, we learn to interpret visual cues, discern between different objects, judge distances, determine motion, and assess image quality. In contrast, computer vision employs cameras, algorithms, and data instead of the biological systems of retinas, optic nerves, and visual cortexes.
However, computer vision offers several distinct advantages over the human eye. For example, a computer vision system trained to inspect items or monitor a production asset can outperform humans in speed and accuracy. It can examine thousands of products or processes per minute while detecting subtle defects or issues invisible to the human eye.
Industries such as energy, utilities, manufacturing, and automobiles all utilize computer vision, and the market for this technology is rapidly expanding. To provide a better understanding of the differences between AI and computer vision, please see the table below:
| AI | Computer Vision |
|---|---|
| ✔ Focuses on problem-solving | ✔ Focuses on visual information |
| ✔ Involves decision making | ✔ Involves image recognition |
| ✔ Generalizes patterns | ✔ Specializes in visual patterns |
| ✔ Used in natural language processing, robotics, and machine learning | ✔ Used in facial recognition, object detection, and image classification |
| ✔ Analyzes data and identifies patterns | ✔ Analyzes images and identifies objects |
| ✔ Can perform tasks without human intervention | ✔ Requires visual inputs and image data |
Computer vision is essential in various industries and has numerous real-world applications, making it an integral part of modern AI technology.
Computer vision requires a lot of data to work correctly. The computer looks at the data repeatedly until it can tell the difference between different things and recognize images. For example, to teach a computer to identify car tires, it needs to see many photos of tires and things related to tires, especially ones without any problems.
Two critical technologies used for this are convolutional neural networks and deep learning, which are types of machine learning. The computer can learn to understand visual input using algorithmic models without being programmed. Given enough data, the computer can teach itself to recognize different images.
A CNN is a technology used in machine learning and deep learning models to help them “see” images by breaking them down into pixels with labels or tags. It makes predictions about what it sees by doing convolutions on the brands, a mathematical operation that creates a new function by combining two existing ones. The neural network keeps doing these convolutions and checking its predictions until they become accurate. This is similar to how humans recognize images, starting with basic shapes and contours and adding details as they look more closely. CNNs are used to analyze individual images, while recurrent neural networks (RNNs) are used for video applications, helping computers understand the relationships between ideas in a sequence.
Computer vision is a powerful technology with many practical applications in various industries.
Let me give you a few examples:
One way computer vision is used is through image classification. This technology can recognize and correctly identify different images, such as a dog, an apple, or even a person’s face. A social media company might use image classification to sort and remove offensive pictures users upload automatically.
Another computer vision application is object detection, which can identify specific objects within an image or video and tabulate their existence. This technology can be used in many fields, including manufacturing, where it can detect damage on an assembly line or locate equipment that needs maintenance.
Once an object is identified, it can be tracked or followed using real-time video streams or a sequence of images. For example, autonomous vehicles must track moving things like pedestrians, other cars, and road infrastructure to avoid accidents and follow traffic regulations.
Content-based image retrieval is another way to use computer vision. Rather than relying on metadata tags attached to photos, this technology can browse, search, and retrieve images from large data repositories. It can also automatically annotate pictures, replacing the need for manual image tagging. This technology can be used in digital asset management systems to improve search and retrieval accuracy.
