Practical OpenCV
(Sprache: Englisch)
Practical OpenCV is a hands-on project book that shows you how to get the best results from OpenCV, the open-source computer vision library.
Computer vision is key to technologies like object recognition, shape detection, and depth...
Computer vision is key to technologies like object recognition, shape detection, and depth...
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Klappentext zu „Practical OpenCV “
Practical OpenCV is a hands-on project book that shows you how to get the best results from OpenCV, the open-source computer vision library. Computer vision is key to technologies like object recognition, shape detection, and depth estimation. OpenCV is an open-source library with over 2500 algorithms that you can use to do all of these, as well as track moving objects, extract 3D models, and overlay augmented reality. It's used by major companies like Google (in its autonomous car), Intel, and Sony; 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 know OpenCV.
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.
Inhaltsverzeichnis zu „Practical OpenCV “
Part 1: Getting comfortableChapter 1: Introduction to Computer Vision and OpenCV
Chapter 2: Setting up OpenCV on your computer
Chapter 3: CV Bling – OpenCV inbuilt demos
Chapter 4: Basic operations on images and GUI windows
Part 2: Advanced computer vision problems and coding them in OpenCV
Chapter 5: Image filtering
Chapter 6: Shapes in images
Chapter 7: Image segmentation and histograms
Chapter 8: Basic machine learning and keypoint-based object detection
Chapter 9: Affine and Perspective transformations and their applications to image panoramas
Chapter 10: 3D geometry and stereo vision
Chapter 11: Embedded computer vision: Running OpenCV programs on the Raspberry Pi
Bibliographische Angaben
- Autor: Samarth Brahmbhatt
- 2013, 1st ed., 244 Seiten, Maße: 19,2 x 23,7 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1430260793
- ISBN-13: 9781430260790
Sprache:
Englisch
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