OpenCfrom 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. WithPractical OpenCV, you'll be able to: or Linuxrun vision algorithms on Raspberry Piscenes in computer vision applications like object detection, image stitching, filtering, stereo vision, and moreprojects for your class/hobby/robot/job, many of which can execute in real time on off-the-shelf processorsdevelop to create your own interactive computer vision app. What you'll learn The ins and outs of OpenCV programming on Windows and LinuxTransforming and filtering images Detecting corners, edges, lines, and circles in images and video Detecting pre-trained objects in images and video Making panoramas by stitching images together Getting depth information by using stereo cameras Basic machine learning techniques BONUS: Learn how to run OpenCV on Raspberry Pi Who this book is for This book is for programmers and makers with little or no previous exposure to computer vision. Some proficiency with C++ is required. Table of Contents Part 1: Getting comfortableChapter 1: Introduction to Computer Vision and OpenCVChapter 2: Setting up OpenCV on your computerChapter 3: CV Bling OpenCV inbuilt demosChapter 4: Basic operations on images and GUI windowsPart 2: Advanced computer vision problems and coding them in OpenCVChapter 5: Image filteringChapter 6: Shapes in imagesChapter 7: Image segmentation and histogramsChapter 8: Basic machine learning and keypoint-based object detectionChapter 9: Affine and Perspective transformations and their applications to image panoramasChapter 10: 3D geometry and stereo visionChapter 11: Embedded computer vision: Running OpenCV programs on the Raspberry Pi"
Description:
OpenCfrom 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. WithPractical OpenCV, you'll be able to: or Linuxrun vision algorithms on Raspberry Piscenes in computer vision applications like object detection, image stitching, filtering, stereo vision, and moreprojects for your class/hobby/robot/job, many of which can execute in real time on off-the-shelf processorsdevelop to create your own interactive computer vision app. What you'll learn The ins and outs of OpenCV programming on Windows and LinuxTransforming and filtering images Detecting corners, edges, lines, and circles in images and video Detecting pre-trained objects in images and video Making panoramas by stitching images together Getting depth information by using stereo cameras Basic machine learning techniques BONUS: Learn how to run OpenCV on Raspberry Pi Who this book is for This book is for programmers and makers with little or no previous exposure to computer vision. Some proficiency with C++ is required. Table of Contents Part 1: Getting comfortableChapter 1: Introduction to Computer Vision and OpenCVChapter 2: Setting up OpenCV on your computerChapter 3: CV Bling OpenCV inbuilt demosChapter 4: Basic operations on images and GUI windowsPart 2: Advanced computer vision problems and coding them in OpenCVChapter 5: Image filteringChapter 6: Shapes in imagesChapter 7: Image segmentation and histogramsChapter 8: Basic machine learning and keypoint-based object detectionChapter 9: Affine and Perspective transformations and their applications to image panoramasChapter 10: 3D geometry and stereo visionChapter 11: Embedded computer vision: Running OpenCV programs on the Raspberry Pi"