Hands-On Computer Vision with OpenCV 4, Keras & TensorFlow 2

Hands-On Computer Vision with OpenCV 4, Keras & TensorFlow 2

Do you want to understand how computers see images and videos? Using artificial intelligence, we can enable computers and smart devices to interpret what is in an image (computer vision).

This can provide massive benefits when it comes to automating tasks for which images are vital, such as examining medical images or enabling self-driving cars to see. Already, these applications are creating a massive industry around computer vision—one that is set to grow rapidly, with some sources predicting that it will be worth over $43 billion by 2023.

This course provides you with a perfect foundation from which to understand computer vision and supports your professional development in this fast-growing arena. We first learn the basic concepts and explore these using OpenCV4, the most popular open-source computer vision library. Next, we explore using Machine Learning in computer vision, including the use of deep learning (using TensorFlow 2.0 and Keras) to implement advanced image classifiers.

This course is designed to help data scientists, and those who already have some familiarity with ML and DL (and experience with Python, Keras, and TensorFlow), to gain a solid understanding of OpenCV and train their own computer vision deep learning models.

About the Author

Rajeev Ratan is a data scientist and computer vision engineer. He has a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence from the University of Edinburgh, where he gained extensive knowledge of Machine Learning, computer vision, and intelligent robotics.

He has published research on using data-driven methods for Probabilistic Stochastic Modeling in the Public Transport arena and was part of a group that won a robotics competition at the University of Edinburgh.

Rajeev launched his own computer vision startup based on using Deep Learning in education. Since then, he has contributed to 2 more startups in computer vision domains and one multinational company in the data science field.

Previously, he worked for 8 years at two of the Caribbean's largest telecommunications operators, where he gained experience in managing technical staff and deploying complex telecommunications projects.

Build your own computer vision deep learning classifiers

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What you will learn

• Image manipulations (dozens of techniques—such as transformations, blurring, thresholding, edge detection, and cropping)
• How to segment images using a variety of OpenCV algorithms, from contouring to blob and line detection
• Approximate contours and perform contour filtering, ordering, and approximations

Rating: 4.35

Level: Beginner Level

Duration: 6 hours

Instructor: Packt Publishing