eBook Practical OpenCV, 1st Edition

  • Published By:
  • ISBN-10: 1430260807
  • ISBN-13: 9781430260806
  • DDC: 006.37
  • Grade Level Range: College Freshman - College Senior
  • 210 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013
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PracticalOpenCV is a hands-on project book that shows you how to get the best resultsfrom OpenCV, the open-source computer vision library. Computer vision is key to technologies like object recognition, shapedetection, and depth estimation. OpenCV is an open-source library with over2500 algorithms that you can use to do all of these, as well as trackmoving objects, extract 3D models, and overlay augmented reality. It’sused by major companies like Google (in its autonomous car), Intel, andSony; and it is the backbone of the Robot Operating System’s computer vision capability.In short, if you’re working with computer vision at all, you need to knowOpenCV.With Practical OpenCV, you’ll be able to: Get OpenCV up and running on Windows or Linux. Use OpenCV to control the camera board and run vision algorithms on Raspberry Pi. Understand what goes on behind the scenes in computer vision applications like object detection, image stitching, filtering, stereo vision, and more. Code complex computer vision projects for your class/hobby/robot/job, many of which can execute in real time on off-the-shelf processors. Combine different modules that you develop to create your own interactive computer vision app.

Table of Contents

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Contents at a Glance.
About the Author.
About the Technical Reviewer.
1: Getting Comfortable.
2: Introduction to Computer Vision and OpenCV.
3: Setting up OpenCV on Your Computer.
4: CV Bling—OpenCV Inbuilt Demos.
5: Basic Operations on Images and GUI Windows.
6: Advanced Computer Vision Problems and Coding Them in OpenCV.
7: Image Filtering.
8: Shapes in Images.
9: Image Segmentation and Histograms.
10: Basic Machine Learning and Object Detection Based on Keypoints.
11: Affine and Perspective Transformations and Their Applications to Image Panoramas.
12: 3D Geometry and Stereo Vision.
13: Embedded Computer Vision: Running OpenCV Programs on the Raspberry Pi.