OpenCV is a native cross-platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for developing applications to process visual data such as photographs or videos. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android, and offers extensive libraries with over 500 functions.
This video demonstrates how to develop a series of intermediate-to-advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this video teach the viewer how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
By the end of this video course, viewers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.
About the Author
Riaz Munshi has a Bachelor's and a Master's degree in Computer Science from the University at Buffalo, NY. He is a computer vision and machine learning enthusiast. Riaz possess over three years' experience working on challenging problems in mobility, computing, and augmented reality. He has a solid foundation in Computer Science, with strong competencies in data structures, algorithms, and software design. Currently he is working at Yahoo as a Software Engineer, exploring use-cases that harness the power of AR in controlling robots. He makes robots perform more efficiently at their job by guiding them remotely via holograms.
In this video, we will be using Hough Transformation to detect lines/circles, or some other basic Shapes in an Image.
This video shows, how to Stretch, Shrink, Warp, and Rotate an Image using OpenCV 3.
In this video, we will be computing image derivatives on images using kernels for edge and blob detection.
In this video, we will segment binary images by extracting contours of arbitrary shapes and sizes.
In this video, we will introduce to techniques like Delaunay Triangulation and Voronoi Tessellation which are widely used to determine the spatial dimension of an object.
In this video, we will learn mean-shift segmentation, and how can we use concept from mean-shift for object tracking, and also getting started with the project for the section.
This video will show applications of computer vision in medical imaging and segmentation. And we will build systems to automatically detect number plates.
In this video, we will learn the concepts behind Harris Corner Detection and implementing Harris Corner Detection from scratch.
This video will make you understand and check the various different algorithms to find features in OpenCV3 like SIFT, SURF, FAST, BRIEF, and ORB.
In this video, we will match features between sequential images using FLANN matcher and also using homography for finding known objects in complex images.
In this video, we will learn about how Mean-Shift and Cam-Shift can be used to track objects in video and Optical flow to trace flow of an image objects in videos.
In this video, we will use Convolutional Neural Nets to learn features from images and learn how to recognize numbers using LeNet-5 architecture.
In this video, we will learn how we can perform visual object recognition using CNNs and we will also implement the project for scene understanding and an automatic labelling from images.
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