Practical OpenCV 3 Image Processing with Python [Video]

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Practical OpenCV 3 Image Processing with Python [Video]

Riaz Munshi

Get familiar with Open CV 3 and learn to build amazing computer vision applications

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Video Details

ISBN 139781787126428
Course Length1 hour and 50 minutes

Video Description

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.

Style and Approach

Boost your knowledge of Computer Vision and image processing by developing real-world projects in OpenCV 3

Table of Contents

Building an Image Search Engine from Scratch
The Course Overview
Learning about Hough Transformations
Stretch, Shrink, Warp, and Rotate Using OpenCV 3
Image Derivatives
Histogram Equalization
Reverse Image Search
Finding Targets and Number Plate Recognition in Video Stream
Extracting Contours from Images
Template Matching for Object Detection
Background Subtraction from Images
Delaunay Triangulation and Voronoi Tessellation
Mean-Shift Segmentation
Medical Imaging and Segmentation
Scene Understanding and Automatic Labeling from Images
Harris Corner Detection
SIFT, SURF, FAST, BRIEF, and ORB Algorithms
Feature Matching and Homography to Recognize Objects
Mean-Shift, Cam-Shift, and Optical Flow
Feature Extraction Using Convolutional Neural Nets (CNNs)
Visual Object Recognition and Classification Using CNNs

What You Will Learn

  • Learn Morphology and Open, Close operations
  • Work with Histogram Generation and Manipulation
  • Discover Background Subtraction from Images
  • Build an Image Search Engine from Scratch based on feature extraction
  • Explore scene understanding and automatic labeling from images
  • Look into Delaunay Triangulation and Voronoi Tessellation
  • Essentials of medical imaging and segmentation
  • Apply image processing techniques

Authors

Table of Contents

Building an Image Search Engine from Scratch
The Course Overview
Learning about Hough Transformations
Stretch, Shrink, Warp, and Rotate Using OpenCV 3
Image Derivatives
Histogram Equalization
Reverse Image Search
Finding Targets and Number Plate Recognition in Video Stream
Extracting Contours from Images
Template Matching for Object Detection
Background Subtraction from Images
Delaunay Triangulation and Voronoi Tessellation
Mean-Shift Segmentation
Medical Imaging and Segmentation
Scene Understanding and Automatic Labeling from Images
Harris Corner Detection
SIFT, SURF, FAST, BRIEF, and ORB Algorithms
Feature Matching and Homography to Recognize Objects
Mean-Shift, Cam-Shift, and Optical Flow
Feature Extraction Using Convolutional Neural Nets (CNNs)
Visual Object Recognition and Classification Using CNNs

Video Details

ISBN 139781787126428
Course Length1 hour and 50 minutes
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