OpenCV 3 – Transforming and Filtering Images
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OpenCV 3 – Transforming and Filtering Images

Build computer vision applications that make the most of the popular C++ library OpenCV 3
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
12 students enrolled
Created by Packt Publishing
Last updated 6/2017
English
Current price: $10 Original price: $125 Discount: 92% off
5 hours left at this price!
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Includes:
  • 1.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Process an image by manipulating its pixels
  • Segment images into homogenous regions and extract meaningful objects
  • Apply image filters to enhance image content
  • Exploit the image geometry in order to relay different views of a pictured scene
View Curriculum
Requirements
  • This video will arm you with the basics you need to start writing world-aware applications right from the pixel level all the way through to processing video sequences.
Description

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.

Who is the target audience?
  • OpenCV 3 – Transforming and Filtering Images is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who wish to be introduced to the concepts of computer vision programming.
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Curriculum For This Course
16 Lectures
01:17:30
+
Transforming Images with Morphological Operations
6 Lectures 23:12

This video gives an overview of the entire course.

Preview 02:16

The ability to present the erosion and dilation of morphological operators.

Eroding and Dilating Images Using Morphological Filters
04:14

Define other operators to clean up an image before extracting its connected components.

Opening and Closing Images Using Morphological Filters
02:24

The ability to present two morphological operators that can lead to the detection of interesting image features.

Applying Morphological Operators on Gray-Level Images
02:56

Learn to use the topological map analogy in the description of the watershed algorithm.

Segmenting Images Using Watersheds
05:05

The ability to extract MSER.

Extracting Distinctive Regions Using MSER
06:17
+
Filtering the Images
5 Lectures 25:05

Learn to present some basic low-pass filters.

Preview 03:37

The ability to reduce the size of an image.

Downsampling Images with Filters
06:23

Learn to use median filter to filter images.

Filtering Images Using a Median Filter
02:32

Perform the opposite transformation to amplify the high-frequency content of an image.

Applying Directional Filters to Detect Edges
06:45

The ability to compute the second-order derivatives to measure the curvature of the image function.

Computing the Laplacian of an Image
05:48
+
Extracting Lines, Contours, and Components
5 Lectures 29:13

The ability to detect the unnecessarily thick edges and detect all important edges of an image.

Preview 04:14

Learn to detect lines in images and detect other simple image structures.

Detecting Lines in Images with the Hough Transform
10:19

Learn how to estimate the exact line that best fits a given set of points.

Fitting a Line to a Set of Points
02:40

The ability to extract the objects that are contained in this collection of 1s and 0s.

Extracting Connected Components
04:57

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.

Computing Components' Shape Descriptors
07:03
About the Instructor
Packt Publishing
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