Practical OpenCV 1st Edition by Samarth Brahmbhatt 9781430260806 1430260807

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ISBN 10:1430260807 
ISBN 13:9781430260806
Author: Samarth Brahmbhatt
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.

Practical OpenCV 1st  Table of contents:

PART 1: Getting Comfortable
CHAPTER 1: Introduction to Computer Vision and OpenCV
Why Was This Book Written?
OpenCV
Summary
CHAPTER 2: Setting up OpenCV on Your Computer
Operating Systems
Summary
CHAPTER 3: CV Bling—OpenCV Inbuilt Demos
Camshift
Stereo Matching
Homography Estimation in Video
Circle and Line Detection
Image Segmentation
Bounding Box and Circle
Image Inpainting
Summary
CHAPTER 4: Basic Operations on Images and GUI Windows
Displaying Images from Disk in a Window
The cv::Mat Structure
Converting Between Color-spaces
GUI Track-Bars and Callback Functions
ROIs: Cropping a Rectangular Portion out of an Image
Accessing Individual Pixels of an Image
Videos
Summary
PART 2: Advanced Computer Vision Problems and Coding Them in OpenCV
CHAPTER 5: Image Filtering
Image Filters
Object Detector App
Morphological Opening and Closing of Images to Remove Noise
Summary
CHAPTER 6: Shapes in Images
Contours
Hough Transform
Generalized Hough Transform
RANdom Sample Consensus (RANSAC)
Bounding Boxes and Circles
Convex Hulls
Summary
CHAPTER 7: Image Segmentation and Histograms
Image Segmentation
Histograms
Summary
CHAPTER 8: Basic Machine Learning and Object Detection Based on Keypoints
Keypoints and Keypoint Descriptors: Introduction and Terminology
SIFT Keypoints and Descriptors
SURF Keypoints and Descriptors
ORB (Oriented FAST and Rotated BRIEF)
Basic Machine Learning
Object Categorization
Summary
CHAPTER 9: Affine and Perspective Transformations and Their Applications to Image Panoramas
Affine Transforms
Perspective Transforms
Panoramas
Summary
CHAPTER 10: 3D Geometry and Stereo Vision
Single Camera Calibration
Stereo Vision
Summary
CHAPTER 11: Embedded Computer Vision: Running OpenCV Programs on the Raspberry Pi
Raspberry Pi
Setting Up Your New Raspberry Pi
Camera board
Usage Examples

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