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...is a collective term for characteristics that the two institutions share.
·-- PUBLISHER -----·> Packt Publishing
·-- LECTURESHIP ---·> OpenCV 3 - Getting started with Image processing
·-- LECTURE DATE --·> 06-2017
05-2017 {Published}
·-- PERIOD in HRS--·> 02+
·-- SCALE ---------·> 13x50mb
·-- WORKING FILES--·> [ ] Included
[■] Without
·-- LECTURE LINK --·> https://goo.gl/nBwCiu
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
In Detail
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 youÆll 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, youÆll 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.