Learning Path: OpenCV: Image Processing with OpenCV 3

Rating 3.7 out of 5 (59 ratings in Udemy)

What you'll learn

Learn how to manipulate pixels
Learn to scan image with pointers and neighbor access
Find out how to compare colors using the strategy design pattern
Understand how to represent colors with hue, saturation, and brightness
Retrieve similar images using the histogram comparison
Learn to segment images into homogenous regions and extract meaningful objects
Exploit the image geometry in order to relay different views of a pictured …

Rating 3.7 out of 5 (59 ratings in Udemy)

What you'll learn

Learn how to manipulate pixels
Learn to scan image with pointers and neighbor access
Find out how to compare colors using the strategy design pattern
Understand how to represent colors with hue, saturation, and brightness
Retrieve similar images using the histogram comparison
Learn to segment images into homogenous regions and extract meaningful objects
Exploit the image geometry in order to relay different views of a pictured scene

Description

OpenCV is a library of programming functions mainly aimed at real-time computer vision. In simple language, it is one of the most powerful library used for image processing. If you wish to learn how to do image processing with OpenCV, then go for this Learning Path.

Packt’s Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

This Learning Path begins with setting up OpenCV library on your system. Then, you will learn how to read and display images. You will also be introduced to basic OpenCV data structures. You will learn how to manipulate pixels and how an image can be read. Also, you will explore different methods to scan an image in order to perform operation on each of its pixels. Next, you will learn how to process the colors of an image where you’ll be presented with various object-oriented design patterns that will help you build better computer vision applications. Also, you will discover how to count pixels with histograms and compute image histograms.

Moving ahead, you will learn different techniques for image enhancement and shape analysis. You will be introduced to the concepts of mathematical morphology and image filtering. Finally, you will learn techniques to achieve camera calibration and perform multiple-view analysis.

By the end of this Learning Path, you will learn to build your own computer vision applications in no time!

About the Author:

For this course, we have combined the best works of this esteemed author:

Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. He co-founded Visual Cortek in 2006, an Ottawa-based video analytics startup that was later acquired by in 2009. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of startups companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.

Duration 3 Hours 58 Minutes

Paid

Self paced

Beginner Level

English (US)

586

Rating 3.7 out of 5 (59 ratings in Udemy)

Go to the Course
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