Computer vision (CV) is a scientific field of artificial intelligence that seeks to develop techniques that train computers to interpret and understand the content of the visual world. Using digital images, videos, and models for deep learning, the machines can accurately identify and classify objects – and react accordingly to the visual input.
History & Recent Developments
In the 1950s, early CV research used some of the earliest neural networks to identify an object’s edges and classify simple shapes into groups like circles and squares. The first commercial application of CV used optical character recognition in the 1970s to translate typed or handwritten text.
Facial recognition software grew in popularity as the internet developed in the 1990s, a time when enormous collections of photographs became accessible online for analysis. These expanding data sets made it possible for computers to recognize specific individuals in images and videos (facial recognition).
Today, several factors contributed to the fast development of CV:
The wide use of smartphones provides a vast amount of photos and videos online.
Powerful computers are easily affordable and accessible.
Computer vision and analysis hardware are widely available.
New algorithms like convolutional neural networks can utilize the capabilities of the hardware and software.
These developments have had astonishing effects on the field of CV. In less than ten years, accuracy rates for object identification and classification have increased rapidly, and modern systems are faster and more accurate than humans at recognizing and responding to visual inputs.
Similar to how you may put together a jigsaw puzzle, computers build together visual images.
Consider how you would solve a jigsaw puzzle. You need to assemble all of the pieces to create an image. That is how CV neural networks function. They can recognize many distinct parts of the image, locate the edges, and after that model the subcomponents. They can put all the pieces of the image together by filtering and taking a sequence of actions through deep network layers, just like you would with a puzzle.
The computer is frequently fed hundreds or thousands of related images to train it to recognize particular objects instead of being given the final image on top of a puzzle box.
For example, programmers upload millions of images of cats, and the model then learns on its own the various characteristics that make up a cat, rather than training computers to look for whiskers, tails, and pointy ears to recognize a cat.
How Computer Vision Works
CV is based on three basic steps:
Acquiring an image
Images, even large sets, can be collected in real-time using video, photos, or 3D technology for analysis.
Processing the image
The majority of this process is automated by deep learning models, but the models are often trained by first being fed a large number of labeled or pre-identified images.
Understanding the image
The final step is the interpretative one, where an object is identified or classified.
Artificial intelligence systems today can even act in response to the image after understanding it. There are numerous varieties of computer vision that are applied in different contexts:
- Image segmentation – a method that divides a digital image into a number of smaller groups known as image segments, which helps to simplify further processing or analysis of the image by bringing down the complexity of the image.
- Object detection – identifying a specific object in an image. Advanced object detection enables the detection of many objects in a single image.
- Facial recognition – an advanced form of object detection that not only spots a person’s face in an image but also recognizes a specific individual.
- Edge detection – a technique of image processing for finding the boundaries of objects within images.
- Pattern recognition – a process of identifying recurring visual elements like shapes, colors, and other indicators in images.
- Image classification – a process of organizing images into different categories.
- Feature matching – establishing correspondences between two images of the same scene/object to help classify them.
Simple computer vision applications might only make use of one of these methods, whereas more complex applications, like computer vision for self-driving cars, depend on a combination of methods to achieve their objectives.
Computer Vision examples
Deep learning for Computer Vision!
Image & Video Analysis and AI!
Facial recognition systems!
This is a broad field of study with numerous specialized methods and tools, as well as specializations to focus on particular application domains.
“Computer vision has a wide variety of applications, both old (e.g., mobile robot navigation, industrial inspection, and military intelligence) and new (e.g., human-computer interaction, image retrieval in digital libraries, medical image analysis, and the realistic rendering of synthetic scenes in computer graphics).”
Computer vision applications
Using computer vision gives significant results in many industries:
- Helps distinguish between staged and real car damage
- Enables facial recognition for security purposes
- Allows automatic checkout possible in retail stores.
- Computer Vision: What it is and why it matters | SAS
- Everything You Ever Wanted To Know About Computer Vision. | by Ilija Mihajlovic
- Introduction To Feature Detection And Matching | by Deepanshu Tyagi | Data Breach | Medium
- Image Segmentation: Part 1. Mathematical and practical… | by Mrinal Tyagi
- What is Facial Recognition – Definition and Explanation
- What Is Object Detection? – MATLAB & Simulink
- Pattern Recognition | Introduction – GeeksforGeeks
- What are Convolutional Neural Networks? | IBM