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...is a collective term for characteristics that the two institutions share.
·-- PUBLISHER -----·> Packt Publishing
·-- LECTURESHIP ---·> Learning Path: OpenCV:
Real-Time Computer Vision with OpenCV
·-- LECTURE DATE --·> 05-2017
05-2017 {Published}
·-- PERIOD in HRS--·> 05+
·-- SCALE ---------·> 51x50mb
·-- LECTURE LINK --·> https://goo.gl/HZqSW5
Practical OpenCV projects
In Detail
Are you looking forward to developing interesting computer vision
applications? If yes, then this Learning Path is for you. Computer
vision and machine learning concepts are frequently used in practical
projects based on computer vision. Whether you are completely new to the
concept of computer vision or have a basic understanding of it, this
Learning Path will be your guide to understanding the basic OpenCV
concepts and algorithms through amazing real-world examples and
projects.
OpenCV is a cross-platform, open source library that is used for face
recognition, object tracking, and image and video processing. Learning
the basic concepts of computer vision algorithms, models, and OpenCVÆs
API will help you develop all sorts of real-world applications.
Starting from the installation of OpenCV 3 on your system and
understanding the basics of image processing, we swiftly move on to
creating optical flow video analysis or text recognition in complex
scenes. YouÆll explore the commonly-used computer vision techniques to
build your own OpenCV projects from scratch. Next, weÆll teach you how
to work with the various OpenCV modules for statistical modeling and
machine learning. YouÆll start by preparing your data for analysis,
learn about supervised and unsupervised learning, and see how to use
them. Finally, youÆll learn to implement efficient models using the
popular machine learning techniques such as classification, regression,
decision trees, K-nearest neighbors, boosting, and neural networks with
the aid of C++ and OpenCV.
By the end of this Learning Path, you will be familiar with the basics
of OpenCV such as matrix operations, filters, and histograms, as well as
more advanced concepts such as segmentation, machine learning, complex
video analysis, and text recognition.
Prerequisites: Knowledge of C++ and Python is required. Some
understanding of statistical concepts would be helpful, but is not
mandatory.
This path navigates across the following products (in sequential order):
OpenCV 3 by Example (3h 57m)
Machine Learning with Open CV and Python (1h 35m)