Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

We can take photos and film a video through the cameras of our smartphones. Furthermore, we can share those images and videos easily through the Internet. Instagram is the biggest example of that. Similarly, on YouTube, people watch billions of videos every day. 

The Internet contains a plethora of images and text. However, to perform the search and find the photos, images that you search on computers follow an algorithm and recognize what the image contains. The computer cannot see. Instead, it just searches according to the defined vectors and representations. This article will help you understand what computer vision is and how it helps the computer to identify images and videos.

What is Computer Vision?

Computer vision is an educational field from computer science. This field helps computers to see, process images, and identify just like we do and provide an accurate output. Computer vision is similar to training a computer about human instincts and intelligence. While this looks like an easy task but making computers understand and recognize the images is challenging. 

How Computer Vision Works

In various tasks, the use of computer vision and other fields of artificial intelligence is standard. All the credit goes to techniques and methods of deep learning. CNN or Conventional Neural Networks are the reason behind what this technology has achieved so far. To build a computer vision through CNN, you need to follow these four steps:

  1. First, you need to create a database after labeling the images or use already labelled images. Annotations help with image recognition and categorization, pairing the classes and the boxes, segmenting the images according to the pixels.
  2. Extract the images and their features according to the tasks. This will help in modeling the problems. 
  3. Training a model according to the features through the deep learning model. This method involves feeding the data consisting of images that help the machine learning model to learn and solve the tasks accordingly.
  4. Evaluation of the model through new images. This step will help in tracking the accuracy of the model.

The above method is an example of supervised machine learning that involves training the computer through strategies and data. 

Applications of Computer Vision

Here are some of the examples of that showcase how this technology can come in handy:

1.Content Organization

In the process of organizing the content, computer vision technology is already helping us. For instance, Apple Photos is a great example of that. It automatically collect the photos and adds tags so that all your data is available in groups allowing you to easily find the photos. With this app, you can get various representations of your best moments.

2.Facial Recognition

Facial recognition helps in recognizing faces by matching with the data. This technology very common and many applications use computer vision for this purpose. For instance, Facebook uses computer vision for identifying people in the photos.

Biometric authentication also uses facial recognition. You can also find smartphones that unlock your screen through this technology. For this purpose, the algorithm uses a front camera. The image is then processed in the mobile devices and analyzes if the person holding the phone is the authorized person or not. 

3.Augmented Reality

Computer vision also helps applications that involve augmented reality. It helps artificial reality applications to detect physical objects. Furthermore, it also defines the depth of the objects in real-time. Using this information, it can create virtual physical environments.

4.Self-driving Cars

 There are some smart vehicles with cameras that capture videos from various angles and share it with the input signal. Computer vision can use that for classification and recognition. Then the system will detect the objects around the vehicle in real-time such as traffic lights, pedestrians, and road mapping. If you are curious about where you can find such cars, then you can check the advanced Tesla car. These are smart cars and also have features such as autopilot. 

How Computer Vision is Different

Let’s understand how computer vision is different than popular fields fields like computer vision, image processing, and machine learning.

1.Image Processing

With image processing, machines process the raw images and transform them. Mostly the algorithm improves the quality of the images and keeps them ready as input for a specific task. However, in computer vision, the main task is to explain and describe the images. For instance, typical components, levels of pixels, rotation of the operations, contrast, noise reduction. On the other hand, components of image processing perform tasks according to the amount of pixels.

2.Machine Vision

Machine vision can help in the manufacturing of the products by perform tasks such as checking sealed product’s s overall quality. 

3.Computer Vision

You can solve various complex problems with the help of computer recognition. For instance, facial recognition is currently used for Snapchat filters. Moreover, biometric identification techniques and Google images also use this method for image analysis.


Computer vision has a great scope in the future and is one of the most popular topics related to new technologies. This technology is different because it uses a different approach when it comes to data. We collect numerous amounts of data daily. Using this data, we can teach computers to learn and understand objects. Computer vision is also an amazing step towards artificial intelligence and enables computers to perform complex activities similar to humans.


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