Python Computer Vision part 2 - Image Representations

Access the complete course: https://academy.zenva.com/product/the-complete-computer-vision-course-with-python/?zva_src=youtube-python-cv

What if you could learn and master some of the same techniques used in:

Self-driving cars
Microsoft Kinect
Google image search
Snapchat and Instagram filters

In this course, you will learn computer vision using Python. You’ll learn about image filtering and processing, pattern recognition, machine learning and face detection.

These technologies are powering the next generation of consumer and enterprise applications.

From the Internet of Things, to advertising and gaming. We want you to get on board and be part of this revolution.

The course includes theory, lots of live coding and examples, and challenges for you to build your portfolio. Challenges include:

Receipt Segmenter – Find text in an image
Currency Counter – Count coins and dollar bills in an image
Multi-object Matching – Find Legend of Zelda rupees using a patter matching algorithm
Face Swap – App to swap the faces of two people

Python Computer Vision Banner-01

Complementing the application is the theory. We’ll discuss some of the mathematics and processes that happen under the hood to give you a better understanding of the concepts.

Colorspace conversions
Segmentation
Filters
Morphology
Edge Detection
Machine Learning
Face detection

At the end of the course, students will learn the fundamental computer vision techniques and be able to apply computer vision and image processing to their own images for a variety of cool tasks like building their own image filters, segmenting images, and even detecting faces in images!

Access +100 programming courses in Zenva: https://zenva.com

Our tutorial blogs: GameDev Academy: https://gamedevacademy.org HTML5 Hive: https://html5hive.org Android Kennel: https://androidkennel.org Swift Ludus: https://swiftludus.org De Idea A App: https://deideaaapp.org

Twitter: @ZenvaTweets ... https://www.youtube.com/watch?v=wc3Z1puldaI