OpenCV Blueprints by Joseph Howse

OpenCV Blueprints Joseph Howse ebook
Publisher: Packt Publishing
ISBN: 9781784399757
Page: 382
Format: pdf

OpenCV: Computer Vision with Python. The release of OpenCV Blueprints is coming closer! Buy OpenCV 3 Blueprints by Joseph Howse, Steven Puttemans, Quan Hua ( ISBN: 9781784399757) from Amazon's Book Store. Blueprints involving OpenCV packagers. Gyroscopic Video Stabilization Video stabilization is a classic problem in computer vision. Shopping for glass There is nothing quite like a well-crafted piece of glass that has survived many years and continued to sparkle because someone cared for it. Application showcase In this section, we show some panorama images captured with our application. 3 Blueprints [help] [order]; Android Application Programming with OpenCV 3 [help] [order] OpenCV for Secret Agents. An OpenCV 3 project based book discussing a range of very interesting computer vision topics. OpenCV 3 Blueprints Copyright © 2015 Packt Publishing All rights reserved. Discover practical and interesting innovations in computer vision while building atop a mature open-source library, OpenCV 3. Launchpad lets projects track the features they intend to implement over the next few releases. Summary This chapter has been an opportunity to consider the inputs of a computer vision system at the hardware and software levels. Capturing data First, we need to create an app for a mobile device that can capture both images and gyroscope signals simultaneously. Efficient Person Identification Using Biometric Properties The rise of digital media is greater than ever. No part of this book may be OpenCV 3 Blueprints. This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. In this section, we'll look at some Android-specific tasks: one is rendering an overlay on top of the camera view and the second is reading media files on Android. Project overview Let's take a moment to understand how the code in this chapter is organized. Classification Once you have extracted the features for all the samples in the dataset, it is time to start the classification process.