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Mastering OpenCV 4 : a Comprehensive Guide to Building Computer Vision and Image Processing Applications with C++, 3rd Edition.

Author: Roy Shilkrot; David Millán Escrivá
Publisher: Birmingham : Packt Publishing Ltd, 2018.
Edition/Format:   eBook : Document : English : 3rd edView all editions and formats
Summary:
Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark ...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Shilkrot, Roy.
Mastering OpenCV 4 : A Comprehensive Guide to Building Computer Vision and Image Processing Applications with C++, 3rd Edition.
Birmingham : Packt Publishing Ltd, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Roy Shilkrot; David Millán Escrivá
ISBN: 9781789539264 1789539269
OCLC Number: 1081000422
Notes: Separate histogram equalization for left and right sides
Description: 1 online resource (272 pages)
Contents: Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Cartoonifier and Skin Color Analysis on the RaspberryPi; Accessing the webcam; Main camera processing loop for a desktop app; Generating a black and white sketch; Generating a color painting and a cartoon; Generating an evil mode using edge filters; Generating an alien mode using skin detection; Skin detection algorithm; Showing the user where to put their face; Implementation of the skin color changer; Reducing the random pepper noise from the sketch image Porting from desktop to an embedded deviceEquipment setup to develop code for an embedded device; Configuring a new Raspberry Pi; Installing OpenCV on an embedded device; Using the Raspberry Pi Camera Module; Installing the Raspberry Pi Camera Module driver; Making Cartoonifier run in fullscreen; Hiding the mouse cursor; Running Cartoonifier automatically after bootup; Speed comparison of Cartoonifier on desktop versus embedded; Changing the camera and camera resolution; Power draw of Cartoonifier running on desktop versus embedded system Streaming video from Raspberry Pi to a powerful computerCustomizing your embedded system!; Summary; Chapter 2: Explore Structure from Motion with the SfM Module; Technical requirements; Core concepts of SfM; Calibrated cameras and epipolar geometry; Stereo reconstruction and SfM; Implementing SfM in OpenCV; Image feature matching; Finding feature tracks; 3D reconstruction and visualization; MVS for dense reconstruction; Summary; Chapter 3: Face Landmark and Pose with the Face Module; Technical requirements; Theory and context; Active appearance models and constrained local models Regression methodsFacial landmark detection in OpenCV; Measuring error; Estimating face direction from landmarks; Estimated pose calculation; Projecting the pose on the image; Summary; Chapter 4: Number Plate Recognition with Deep Convolutional Networks; Introduction to ANPR; ANPR algorithm; Plate detection; Segmentation; Classification; Plate recognition; OCR segmentation; Character classification using a convolutional neural network; Creating and training a convolutional neural network with TensorFlow; Preparing the data; Creating a TensorFlow model; Preparing a model for OpenCV Import and use model in OpenCV C++ codeSummary; Chapter 5: Face Detection and Recognition with the DNN Module; Introduction to face detection and face recognition; Face detection; Implementing face detection using OpenCV cascade classifiers; Loading a Haar or LBP detector for object or face detection; Accessing the webcam; Detecting an object using the Haar or LBP classifier; Detecting the face; Implementing face detection using the OpenCV deep learning module; Face preprocessing; Eye detection; Eye search regions; Geometrical transformation

Abstract:

Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the  Read more...

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