Roadmap To Learn Computer Vision in 2022

 There are numerous amount of resources out these days to learn Computer Vision. But the main question arises that what all things and in what manner should I learn.

In this article, we will see steps to learn computer vision.

Table of Contents

·   What is Computer Vision?

·   Scope in Computer Vision

·   Roadmap to Computer Vision

·   Conclusion and Points to Remember

What is Computer Vision?

Computer Vision is the part of the Artificial Intelligence (A.I) field

which extracts and gains a high-level understanding of digital images or videos. From an engineering point of view, it seeks to understand and automate tasks that the human visual system can do.

It includes activities or tasks like acquiring, processing, analyzing, understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information.

It also includes the transformation of visual images into descriptive information.

Scope In Computer Vision Field

Computer vision is a part of Artificial Intelligence (A.I) and is a growing Industry in the I.T Sector. Computer vision is a vast subfield and is also used in the Medical and Military sectors so there are many jobs available in this field and it grows every year.

A Computer Vision Engineer earns an avg of 120,000 dollars in the U.S and 5lakhs P.A in India.

Roadmap to Computer Vision

There are many topics to learn about Computer Vision. Below are the topics of Computer Vision that you need to learn step by step.

1.   Maths

2.   Programming Language

3.   Machine Learning Algorithms

4.   Solving Image Classification Problem

5.   Neural Networks

6.   Digital Image Processing

7.   Natural Language Processing

8.   Video Analytics

9.   Explore Deep Learning Tools

10.   Object Detection Problems

11.   Making Projects

1. Mathematics

Learning Basic to advanced maths is the 1st and the most important step toward Computer Vision. You should start learning topics like Probability, Statistics, Linear Algebra, and Calculus. Ensure that u know these topics very well before jumping to another step.

2. Programming Language

Programming language is another important step in any of the engineering fields. You should stick to a particular language and expertise in it. Python would be the most preferred language as it is fast and easy and there are a variety of libraries in the field of data science and Artificial Intelligence (A.I). Libraries in Python include NumPy, Pandas, skimage, pillow, OpenCV, TensorFlow, and many more...

3. Machine Learning

You should also be familiar with the basics of Machine Learning. Nowadays Artificial Intelligence with machine learning is the most essential as the market and the field is in a boom. One of the famous examples of machine learning with computer vision is Augmented Reality (AR). Start with the basics of machine learning shown below :

·   Deep Learning

·   Data Mining

·   Linear Regression

·   Logistic Regression

·   Concept of Underfitting and Overfitting

4. Solving Image Classification Problem

Now you will be having a basic understanding of Machine Learning, Start trying different problems with image pre-processing techniques. Also, you should be able to solve image classification problems using Machine Learning Models.

5. Neural Networks

Neural Networks, also known as artificial neural networks (ANNs) relays on training data to learn and improve their accuracy over time. It is the heart of Deep Learning Algorithms. If the algorithms are fine-tuned, they are powerful tools in Artificial Intelligence (A.I). Python and R can also be useful in Neural Networks because of their libraries and packages.

6. Digitial Image Processing

Digital Image Processing(DIP) is the use of software to manipulate images. It includes Image Processing, Image Compression, Digital Grayscaling, and many more. There are various projects in this field with computer visions that u can go check on Github. We can also say that without Digital Image Processing, there is no computer vision. So make sure that u learn it thoroughly and make projects on it.

7. Natural Language Processing

Natural Language Processing (NLP) is part of Artificial Intelligence (A.I). It gives the computers the ability to understand text and spoken words. The best example is Google Assistant which uses this technology.

This is where u can specialize and make your computer vision career a new way. Start learning and making projects in this area and flaunt it on your web page or social media accounts.

8. Video Analytics

Video Analytics is the best application of computer vision. The demand for video analytics is not only increasing in this field but also in many other fields. You should have a basic knowledge of how to work with different kinds of datasets.

9. Explore Deep Learning Tools

If you have come this far, you have covered a lot of things in this field and

it's time for exploring Deep Learning Tools. There are a number of tools but I recommend 2 tools- PyTorch and TensorFlow as they are the most common ones. Try applying all the concepts that u have learned till now in either of these tools.

10. Object Detection Problems

Object Detection is widely used in the field of computer vision and everyday life. Cameras, Face Recognition are some of the Object Detection techniques which you all know. Get familiar with object detection algorithms. Start taking notes on the errors u get so that u will not make the same mistakes again and again.

11. Making Projects

Now that u have completed all the concepts that are needed for computer vision......start making projects with all the concepts. Projects making on your own gives a boost to your resume and companies tend to hire those u have done something in the field.....so get started. Don't think that u are new so u can't do it or how well you do it. You just start trying and observe the mistakes you are making by noting them down so that u won't repeat them. Experience not only comes with Internships or Jobs.... but it also comes through exploring and making projects.

Conclusion and Points to Remember

Congrats!!!!! on reaching this point and becoming a computer vision scientist. Remember this is a vast and growing field so even if you have learned all the concepts, there will be more to learn, so keep updated yourself. Don't be overconfident, do your work calmly and peacefully.

Also, there is no harm in learning your neighboring fields in Artificial Intelligence (A.I) cause u never know what new things will come in near future....or u can use your brains and combine the fields of Artificial Intelligence and make the world more advanced. Making mistakes is a part of life...so don't get demotivated, just keep trying until you succeed.

Also, keep showcasing of your projects on social media and if you have a web page .....put it there too. Hope you Ace your interviews!!! Best of Luck.