The 5 best computer vision apps

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  • Computer vision is the technology that allows the digital world to interact with the real world.
  • Explore 5 of the most popular Computer Vision applications
    • Pose estimation using artificial vision
    • Image transformation using Gans
    • Computer vision for the development of social distancing tools
    • Converting 2D images to 3D models
    • Medical Image Analysis

I started using Facebook ago 10 years. in addition, if you used it a long time ago you must remember manual photo tagging. But now we don't have to tag these images manually. Facebook recognizes most of the people in the uploaded image and offers suggestions for tagging them. In the same way, you must have seen those funny filters on snapchat where people use the dog filter and put on a dog face. Have you ever wondered how all this is possible? How can our phone detect our face and add filters on it? These are some of the artificial vision applications.

Computer vision is one of the hottest research fields in the world of data science.. What's more, has become part of our personal life. Knowingly or unknowingly, we all use various functions that have computer vision techniques running in the backend. For instance, we use face unlock on our smartphones. The following picture efficiently explains how face detection works.



I choose face detection to start this article, since this is the only computer vision application that we have all seen. But believe me, computer vision is not limited to this. In this article, explore more interesting applications of computer vision.

If you are looking to master computer vision, check our course Computer Vision Using Deep Learning 2.0

Table of Contents

  • What is Computer Vision?
  • Pose estimation using artificial vision
  • Image transformation using Gans
  • Computer vision for the development of social distancing tools
  • Converting 2D images to 3D models
  • Medical Image Analysis

What is Computer Vision?

Before we enter the world of computer vision applications, first, let's understand what computer vision is. In summary, computer vision is a multidisciplinary branch of artificial intelligence that attempts to replicate the powerful capabilities of human vision.

If we go through the formal definition,

“Computer vision is a utility that makes useful decisions about real physical objects and scenes based on detected images” (Sockman y Shapiro, 2001)

Computer vision works through visual recognition techniques such as image classification, object detection, image segmentation, object tracking, optical character recognition, image captions, etc. I know these are a lot of technical terms, but understanding them is not difficult. Just look at the image below and you will understand many of these terminologies.


source: https: //

Let's start with the first image. If i ask you, what's in the picture? Your answer will be, it is a cat. This is the classification. That means tagging the image based on its rating.. Here the class is 'Cat'.

Now you know the class of the image. The next question is where the object is in the image. When we identify the location of the object in the frame and create a bounding box around it, It is known as location. In the second image, we identify the location of the object and label it as a cat.

The next term is object detection. In the two previous cases, we have only one object in the image, but what if there are multiple objects present? Here we identify the present instances and their location using bounding boxes.

On object detection, we use a bounding box of square or rectangular shape, but it says nothing about the shape of the objects. Instance Segmentation creates a pixel mask around each object. Therefore, instance segmentation provides a deeper understanding of the image.

Check out the following resources to learn more about Computer Vision-

Recent developments

Recent developments in deep learning approaches and advances in technology have greatly increased the capabilities of visual recognition systems.. As a result, companies have quickly embraced computer vision. Successful computer vision use cases can be seen in industrial sectors leading to scaling up applications and increasing demand for computer vision tools.

Now, without wasting any more time, let's move on to 5 exciting applications of computer vision.

Estimation of human posture

Estimating human posture is an interesting application of computer vision. You must have heard of Posenet, which is an open source model for estimating human pose. In summary, pose estimation is a computer vision technique to infer the pose of a person or object present in the image / video.

Before discussing how pose estimation works, first let's understand the ‘Human Pose Skeleton’. It is the set of coordinates to define the pose of a person. A pair of coordinates is known as a limb. What's more, the estimation of the pose is carried out by identifying, locating and tracking key points of the human pose skeleton in an image or video.


source: https: //’DEEP’_Landing_Error_Scoring_System

The following are some of the applications of human pose estimation:

  • Activity recognition for real-time sports analysis or surveillance system.
  • For augmented reality experiences
  • In Robot Training
  • Animation and games

The following are some data sets if you want to develop a pose estimation model yourself:

I found DeepPose from Google as a very interesting research paper that uses deep learning models for the estimation of poses. To go deeper, you can visit various investigations documents available in pose estimate

Image transformation using GAN:

Faceapp is a very interesting and fashionable application among people. It is an image manipulation tool and transforms the input image using filters. Filters can include age or exchange filter of a recent genre.


source: https: //

Look at the picture above, truth? A few months ago it was a hot topic on the internet. People were sharing pictures after changing their gender. But, What is the technology behind these applications? Yes, you guessed it correctly, es Computer Vision, to be more specific, their adversarial generative networks of deep convolution.

Antagonistic generative networks popularly known as GAN is an exciting innovation in the field of computer vision. Although GANs are an old concept, in its present form was proposed by Ian Goodfellow in 2014. Since then it has undergone many developments.

GAN training involves two neural networks facing each other to generate new data based on the distribution of the given training data. Although originally proposed as an unsupervised learning mechanism, GANs have proven to be a good candidate for supervised and semi-supervised learning.

To know more about how Gans works, see article below.

The following are some must-read research articles on GAN that I personally recommend:

The following are some data sets to help you gain hands-on experience with GANs:


When it comes to discussing Gans Imaging applications, we have many. The following are some of its applications:

  • Image-to-image translation in style transfer and photo in painting
  • Super image resolution
  • Text-to-image generation
  • Image editing
  • Semantic image-to-photo translation

If you find something more interesting, let me know in the comment section.

Computer vision to develop social distancing tools

During the last months, the world suffers from the COVID-19 pandemic. It is found that until the disease vaccine is available, we should all take the precautionary measures of using hand sanitizers, mask and the most important thing is the monitoring of social distancing.

Computer vision technology can play a vital role in this crucial scenario. It can be used to track people in a particular premise or area to find out if they are following social distancing rules or not.

The social distancing tool is a real-time object detection and tracking application. In this case, to verify the violation of social distancing, we detect each person present in the video by means of a bounding box. Later we track the movement of each box in the frame and calculate the distance between them. If you detect any violation of the social distancing rule, highlight those bounding boxes.


What's more, to make these tools more advanced and accurate, can use transfer learning techniques. Various previously trained object detection models such as YOLO O Máscara R-CNN they are also there.

The following article helps you create a social distancing tool for yourself.:

Create a 3D model from 2D images

Here is another very interesting application of computer vision. You are converting two-dimensional images into 3D models. For instance, imagine you have a photograph from your old collection and you can transform it into a 3D model and inspect it as if you were there.


source: https: //

Deep Mind researchers have created an artificial intelligence system that works along similar lines. It is known as Red's generative resolutions, Can perceive images from different angles like humans.

What's more, Nvidia has developed an artificial intelligence architecture that can predict 3D properties from an image. Similarly, Facebook AI offers a similar tool known as 3D photo function.

The following are some relevant data sets available for you to experiment with.:

What's more, check are interesting articles to know more about the application.


Now you must be thinking about the use cases of this technology. The following are its applications:

  • Animation and games
  • Robotics
  • Autonomous cars
  • Medical diagnosis and surgical operations.

Computer Vision in Healthcare: medical image analysis

For a long time, computer-aided medical images are used for diagnosis such as CT scans, X-rays, etc. What's more, recent developments in computer vision technologies allow clinicians to better understand them by turning them into interactive 3D models and making their interpretation easy.

If we look at the most recent use case for computer vision, we will find out you are detecting COVID-19 cases using a chest x-ray. What's more, according to a study by Radiology Department, Wuhan, deep learning methods can be used efficiently to distinguish Covid-19 from community-acquired pneumonia.

Review the COVID chest x-ray -19 Kaggle dataset and get your hands dirty on implementation.


Meanwhile, if you want to work on another dataset, has Medical CT images also available at Kaggle. What's more, if you want to know more about medical imaging and its applications in healthcare, lea are research works and their implementations.

Final notes

In summary, computer vision is a fascinating field of artificial intelligence. Name the field and you will get a CV request there. In this article, I talked about some of them that I found interesting. But this is only the tip of the iceberg.

In case you are interested in knowing how to have a career in Computer Vision, read the following-

Now it's your turn to start implementing computer vision on your own.. Don't forget to share your favorite machine vision app in the comment box.

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