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OpenCV 3 Computer Vision with Python Cookbook

You're reading from OpenCV 3 Computer Vision with Python Cookbook Leverage the power of OpenCV 3 and Python to build computer vision applications

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781788474443

Length 306 pages

Edition 1st Edition

Languages

Python

Tools

OpenCV

Concepts

Computer Vision

Authors (2):

Aleksandr Rybnikov

Aleksei Spizhevoi

View More author details

Table of Contents (11) Chapters

Preface

1. I/O and GUI FREE CHAPTER

2. Matrices, Colors, and Filters

3. Contours and Segmentation

4. Object Detection and Machine Learning

5. Deep Learning

6. Linear Algebra

7. Detectors and Descriptors

8. Image and Video Processing

9. Multiple View Geometry

10. Other Books You May Enjoy

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Image and Video Processing

This chapter contains recipes for:

Warping an image using affine and perspective transformations
Remapping an image using arbitrary transformation
Tracking keypoints between frames using the Lucas-Kanade algorithm
Background subtraction
Stitching many images into panorama
Denoising a photo using non-local means algorithms
Constructing an HDR image
Removing defects from a photo with image inpainting

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Authors (2)

Aleksei Spizhevoi

Aleksei Spizhevoi

Alexey Spizhevoy has over 7 years of experience in computer vision R&D. He has worked for 5 years at Itseez, the main OpenCV contributor, before it was acquired by Intel. He has contributed to video stabilization and photo stitching modules into OpenCV library. He has successfully participated in numerous Computer Vision projects in such areas as 3D reconstruction, video conferencing, object detection and tracking, semantic segmentation, driving assistance, and others. He holds a master's degree in computer science, and he is currently pursuing PhD.

See other products by Aleksei Spizhevoi

Rybnikov

Rybnikov

Aleksandr Rybnikov has over 5 years of experience in C++ programming, including 3 years in the Computer Vision (CV) domain. He worked at Itseez, a company that supported and developed OpenCV, and then at Intel. He enriched OpenCV's dnn module by adding support of another two Deep Learning (DL) frameworks and many features, along with improving the existing functionality. As an engineer, he participated in CV and DL projects such as iris recognition, object detection, semantic segmentation, 6-DOF pose estimation, and digital hologram reconstruction. He has a master's degree in physics.

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