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Practical OpenCV.

By: Brahmbhatt, Samarth.
Material type: TextTextSeries: eBooks on Demand.Publisher: Dordrecht : Springer, 2013Description: 1 online resource (229 p.).ISBN: 9781430260806.Subject(s): Artificial intelligence | Computer science | Object-oriented programming languagesGenre/Form: Electronic books.Additional physical formats: Print version:: Practical OpenCVDDC classification: 006.42 LOC classification: QA76.73.C154 .B384 2013Online resources: Click here to view this ebook.
Contents:
Contents at a Glance; Part 1: Getting Comfortable; Chapter 1: Introduction to Computer Vision and OpenCV; Why Was This Book Written?; OpenCV; History of OpenCV; Built-in Modules; Summary; Chapter 2: Setting up OpenCV on Your Computer; Operating Systems; Ubuntu; Simple Install; Customized Install (32-bit); Customized Install (64-bit); Checking the Installation; Installing Without Superuser Privileges; Using an Integrated Development Environment; Windows; Mac OSX; Summary; Chapter 3: CV Bling-OpenCV Inbuilt Demos; Camshift; Stereo Matching; Homography Estimation in Video
Circle and Line DetectionImage 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; Creating a cv::Mat; Accessing elements of a cv::Mat; Expressions with cv::Mat; Converting Between Color-spaces; GUI Track-Bars and Callback Functions; Callback Functions; ROIs: Cropping a Rectangular Portion out of an Image; Region of Interest in an Image; Accessing Individual Pixels of an Image; Exercise; Videos; Displaying the Feed from Your Webcam or USB Camera/File
Writing Videos to DiskSummary; Part 2: Advanced Computer Vision Problems and Coding Them in OpenCV; Chapter 5: Image Filtering; Image Filters; Blurring Images; Resizing Images-Up and Down; Eroding and Dilating Images; Detecting Edges and Corners Efficiently in Images; Edges; Canny Edges; Corners; Object Detector App; Morphological Opening and Closing of Images to Remove Noise; Summary; Chapter 6: Shapes in Images; Contours; Point Polygon Test; Hough Transform; Detecting Lines with Hough Transform; Detecting Circles with Hough Transform; Generalized Hough Transform
RANdom Sample Consensus (RANSAC)Bounding Boxes and Circles; Convex Hulls; Summary; Chapter 7: Image Segmentation and Histograms; Image Segmentation; Simple Segmentation by Thresholding; Floodfill; Watershed Segmentation; GrabCut Segmentation; Histograms; Equalizing Histograms; Histogram Backprojections; Meanshift and Camshift; Summary; Chapter 8: Basic Machine Learning and Object  Detection Based on Keypoints; Keypoints and Keypoint Descriptors: Introduction and Terminology; General Terms; How Does the Keypoint-Based Method Work?; SIFT Keypoints and Descriptors
Keypoint Detection and Orientation EstimationSIFT Keypoint Descriptors; Matching SIFT Descriptors; SURF Keypoints and Descriptors; SURF Keypoint Detection; SURF Descriptor; ORB (Oriented FAST and Rotated BRIEF); Oriented FAST Keypoints; BRIEF Descriptors; Basic Machine Learning; SVMs; Object Categorization; Strategy; Organization; Summary; Chapter 9: Affine and Perspective Transformations and Their Applications to Image Panoramas; Affine Transforms; Applying Affine Transforms; Estimating Affine Transforms; Perspective Transforms; Panoramas; Summary; Chapter 10: 3D Geometry and Stereo Vision
Single Camera Calibration
Summary: 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
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QA76.73.C154 C# Cookbook. QA76.73.C154 C# 24-Hour Trainer. QA76.73 .C154 A418 2010 C# Essentials. QA76.73.C154 .B384 2013 Practical OpenCV. QA76.73.C154 .O384 2013 C++ Quick Syntax Reference. QA76.73.C154 .W384 2012 Beginning Visual C# 2012 programming / QA76.73.C25 L363 2014 Programming in COBOL :

Contents at a Glance; Part 1: Getting Comfortable; Chapter 1: Introduction to Computer Vision and OpenCV; Why Was This Book Written?; OpenCV; History of OpenCV; Built-in Modules; Summary; Chapter 2: Setting up OpenCV on Your Computer; Operating Systems; Ubuntu; Simple Install; Customized Install (32-bit); Customized Install (64-bit); Checking the Installation; Installing Without Superuser Privileges; Using an Integrated Development Environment; Windows; Mac OSX; Summary; Chapter 3: CV Bling-OpenCV Inbuilt Demos; Camshift; Stereo Matching; Homography Estimation in Video

Circle and Line DetectionImage 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; Creating a cv::Mat; Accessing elements of a cv::Mat; Expressions with cv::Mat; Converting Between Color-spaces; GUI Track-Bars and Callback Functions; Callback Functions; ROIs: Cropping a Rectangular Portion out of an Image; Region of Interest in an Image; Accessing Individual Pixels of an Image; Exercise; Videos; Displaying the Feed from Your Webcam or USB Camera/File

Writing Videos to DiskSummary; Part 2: Advanced Computer Vision Problems and Coding Them in OpenCV; Chapter 5: Image Filtering; Image Filters; Blurring Images; Resizing Images-Up and Down; Eroding and Dilating Images; Detecting Edges and Corners Efficiently in Images; Edges; Canny Edges; Corners; Object Detector App; Morphological Opening and Closing of Images to Remove Noise; Summary; Chapter 6: Shapes in Images; Contours; Point Polygon Test; Hough Transform; Detecting Lines with Hough Transform; Detecting Circles with Hough Transform; Generalized Hough Transform

RANdom Sample Consensus (RANSAC)Bounding Boxes and Circles; Convex Hulls; Summary; Chapter 7: Image Segmentation and Histograms; Image Segmentation; Simple Segmentation by Thresholding; Floodfill; Watershed Segmentation; GrabCut Segmentation; Histograms; Equalizing Histograms; Histogram Backprojections; Meanshift and Camshift; Summary; Chapter 8: Basic Machine Learning and Object  Detection Based on Keypoints; Keypoints and Keypoint Descriptors: Introduction and Terminology; General Terms; How Does the Keypoint-Based Method Work?; SIFT Keypoints and Descriptors

Keypoint Detection and Orientation EstimationSIFT Keypoint Descriptors; Matching SIFT Descriptors; SURF Keypoints and Descriptors; SURF Keypoint Detection; SURF Descriptor; ORB (Oriented FAST and Rotated BRIEF); Oriented FAST Keypoints; BRIEF Descriptors; Basic Machine Learning; SVMs; Object Categorization; Strategy; Organization; Summary; Chapter 9: Affine and Perspective Transformations and Their Applications to Image Panoramas; Affine Transforms; Applying Affine Transforms; Estimating Affine Transforms; Perspective Transforms; Panoramas; Summary; Chapter 10: 3D Geometry and Stereo Vision

Single Camera Calibration

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

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