Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even find the right colors for your redecoration.
This course provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in the image and video analysis that will enable you to build your own computer vision applications. This video helps you to get started with the library and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices.
Moving on, you will learn how to read and write images and manipulate their pixels. We’ll present different techniques for image enhancement and shape analysis. You will learn how to detect specific image features such as lines, circles, or corners. Then, you’ll be introduced to the concepts of mathematical morphology and image filtering. We describe the most recent methods for image matching and object recognition, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Next, we explain techniques to achieve camera calibration and perform a multiple-view analysis. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification.
About the 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 start-up. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of start-up companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.
Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987) and MSc and PhD degrees from INRS-Telecommunications, Montreal (1996). You can visit the author’s website at laganiere.name.
The ability to present the erosion and dilation of morphological operators.
Define other operators to clean up an image before extracting its connected components.
The ability to present two morphological operators that can lead to the detection of interesting image features.
Learn to use the topological map analogy in the description of the watershed algorithm.
The ability to extract MSER.
The ability to reduce the size of an image.
Learn to use median filter to filter images.
Perform the opposite transformation to amplify the high-frequency content of an image.
The ability to compute the second-order derivatives to measure the curvature of the image function.
The ability to detect the unnecessarily thick edges and detect all important edges of an image.
Learn to detect lines in images and detect other simple image structures.
Learn how to estimate the exact line that best fits a given set of points.
The ability to extract the objects that are contained in this collection of 1s and 0s.
Learn to identify the object or to compare it with other image elements. It can be useful to perform some measurements on the component in order to extract some of its characteristics.
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