Computer Vision Beginner Resources

by Gogul Ilango

December 9, 2018

Contents

If We Want Machines to Think, We Need to Teach Them to See - Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab.

Computer Vision is a broad field that has gained massive attraction after AlexNet competed in ImageNet Large Scale Visual Recognition Challenge (2012). Below you can see the Google trend of Computer Vision keyword being searched over the past 5 years.

Just like any other domain in Artificial Intelligence, Computer Vision is a challenging domain as it involves making computers understand and extract meaning from the outside world. Knowledge on Computer Vision makes one to solve problems in key areas such as Agriculture, Healthcare, Banking, Automotive, Retail, Industrial usecases and Security - read more.

In this blog post, you will find resources such as lectures, libraries, blogs, courses and educational content related to applying Artificial Intelligence in Computer Vision for beginners. If you find out any interesting or valuable content, kindly add it in the comment section below so that we will add it here.

Note: This blog post is for absolute beginners who are new to Computer Vision and Artificial Intelligence.

CV Lectures

Computer Vision (CV) lectures taught by top universities such as Stanford, UCF etc., are provided for free in YouTube for people interested to learn. A beginner in Computer Vision can start here to understand the core concepts.

Python Courses

Majority of CV problems are currently solved using Python, although production-level deployment might involve C++ as well. Hence, knowledge on Python is crucial for beginners to launch their career in CV. OpenCV is the de facto standard when it comes to solving Computer Vision problems.

ML/DL Courses

Currently, to solve Computer Vision problems, Machine Learning (ML) and Deep Learning (DL) techniques are highly preferred. This is due to the accuracy these algorithms bring in solving current state-of-the-art CV problems. Hence, learning CV requires you to learn ML and DL concepts too!

PyImageSearch

To learn practical computer vision using Python and OpenCV, Adrian Rosebrock has excellent courses and books which I felt is completely worth it. I highly encourage beginners to enroll in his courses, buy his valuable books on CV and follow his amazing CV blog.

Blogs

There are so many CV blogs that deliver top-notch tutorials on how to use a specific library or how to solve a CV problem using code or describing current state-of-the-art research in CV. Reading blogs is one of the ways to catch up with the fast-moving CV industry.

Individual Blogs

Organization Blogs

Libraries

When it comes to code, libraries becomes handy! Python is the programming language of choice which has rich ecosystem of libraries for CV, ML, DL and other data science techniques such as Data Manipulation and Data Visualization.

Datasets

When it comes to making a computer learn images/videos, we need datasets. There are publicly available datasets around the internet to benchmark algorithms for a specific problem.

Bulk Dataset Links

Image Classification

Scene Recognition

Image Captioning

Pedestrian Detection

Fine-grained Classification

Books

Some of the books that I read regularly and refer, related to Computer Vision, Deep Learning and Machine Learning are as follows.

Bonus Resources

Below are some additional resources that you could use to learn more about Computer Vision, Machine Learning and Deep Learning.

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