Posted on 2017-06-13, by voska89.
OpenCV 3 - Getting started with Image processing
Size: 637.40 MB | Duration: 2 hours | Video: AVC (.mp4) 1920x1080 25fps | Audio: AAC 44KHz 2ch
Genre: eLearning | Language: English
Videos to help you build computer vision applications that make the most of the popular C++ library OpenCV 3!
About This Video
* Install OpenCV library
* Access pixel values
* Scan an image with pointers and neighbor access
* Compare colors using the strategy design pattern
* Segment an image with the GrabCut algorithm
* Represent colors with hue, saturation, and brightness
* Compute and Equalize image histogram
* Retrieve similar images using the histogram comparison
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 help you 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 image 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. You will learn how to read and display images. It also introduces the basic OpenCV data structures.
Moving on, you will see how to manipulate pixels, and how an image can be read. This section explores different methods to scan an image in order to perform an operation on each of its pixels.
After that, you will find out how to process the colors of an image, where you'll be presented with various object-oriented design patterns that will help you to build better computer vision applications. This section also shows you the concept of colors in images.
Finally, you'll discover how to count pixels with histograms, how to compute image histograms, and how they can be used to modify an image. This section presents different applications based on histograms so you can achieve image segmentation, object detection, and image retrieval.
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