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首页深度解析OpenCV实践项目:高清英文版
深度解析OpenCV实践项目:高清英文版
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更新于2024-07-17
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"《深入理解OpenCV:实用计算机视觉项目解析》是一本专为计算机视觉技术爱好者和专业人士精心编撰的高清英文版教程。本书由Daniel Lélis Baggio、Shervin Emami、David Millán Escrivá等多位作者共同编写,涵盖了广泛的实际项目案例,旨在帮助读者逐步解决从增强现实、车牌识别到人脸识别、3D头跟踪等各种常见且实用的计算机视觉问题。读者无论是在桌面开发还是移动应用领域,都能在本书中找到针对性的解决方案。 这本书以实践为导向,通过一系列step-by-step的教学方法,引导读者深入理解OpenCV库的工作原理,以及如何将其应用于实际场景。OpenCV(Open Source Computer Vision Library)是一个开源的计算机视觉和机器学习库,它为开发人员提供了丰富的图像处理和计算机视觉算法,对于提高图像分析、物体检测和识别能力具有重要作用。 版权方面,该书受2012年Packt Publishing出版,所有内容未经事先书面许可不得复制、存储或任何形式传播,除非在学术文章或评论中进行简短引用。尽管作者和出版社尽力确保信息的准确性,但书中提供的信息不带有任何形式的保修,作者、Packt Publishing及其经销商和分销商不对由此书引起的直接或间接损害承担法律责任。 《深入理解OpenCV:实用计算机视觉项目解析》是一本极具价值的学习资源,适合希望提升计算机视觉技能的开发者,无论是为了个人兴趣还是职业发展,都能从中获益匪浅。通过跟随书中的实例,读者可以扎实掌握OpenCV的核心功能,并将其运用到自己的项目中,从而推动技术创新和应用创新。"
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Table of Contents
[ iii ]
Creating OpenGL windows using OpenCV 118
Video capture using OpenCV 118
Rendering augmented reality 119
ARDrawingContext.hpp 119
ARDrawingContext.cpp 120
Demonstration 122
main.cpp 123
Summary 126
References 127
Chapter 4: Exploring Structure from Motion Using OpenCV 129
Structure from Motion concepts 130
Estimating the camera motion from a pair of images 132
Point matching using rich feature descriptors 132
Point matching using optical ow 134
Finding camera matrices 139
Reconstructing the scene 143
Reconstruction from many views 147
Renement of the reconstruction 151
Visualizing 3D point clouds with PCL 155
Using the example code 158
Summary 159
References 160
Chapter 5: Number Plate Recognition Using SVM and
Neural Networks 161
Introduction to ANPR 161
ANPR algorithm 163
Plate detection 166
Segmentation 167
Classication 173
Plate recognition 176
OCR segmentation 177
Feature extraction 178
OCR classication 181
Evaluation 185
Summary 188
Chapter 6: Non-rigid Face Tracking 189
Overview 191
Utilities 191
Object-oriented design 191
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Table of Contents
[ iv ]
Data collection: Image and video annotation 193
Training data types 194
Annotation tool 198
Pre-annotated data (The MUCT dataset) 198
Geometrical constraints 199
Procrustes analysis 202
Linear shape models 205
A combined local-global representation 207
Training and visualization 209
Facial feature detectors 212
Correlation-based patch models 214
Learning discriminative patch models 214
Generative versus discriminative patch models 218
Accounting for global geometric transformations 219
Training and visualization 222
Face detection and initialization 224
Face tracking 228
Face tracker implementation 229
Training and visualization 231
Generic versus person-specic models 232
Summary 233
References 233
Chapter 7: 3D Head Pose Estimation Using AAM and POSIT 235
Active Appearance Models overview 236
Active Shape Models 238
Getting the feel of PCA 240
Triangulation 245
Triangle texture warping 247
Model Instantiation – playing with the Active Appearance Model 249
AAM search and tting 250
POSIT 253
Diving into POSIT 253
POSIT and head model 256
Tracking from webcam or video le 257
Summary 259
References 260
Chapter 8: Face Recognition using Eigenfaces or Fisherfaces 261
Introduction to face recognition and face detection 261
Step 1: Face detection 263
Implementing face detection using OpenCV 264
Loading a Haar or LBP detector for object or face detection 265
Accessing the webcam 266
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Table of Contents
[ v ]
Detecting an object using the Haar or LBP Classier 266
Detecting the face 268
Step 2: Face preprocessing 270
Eye detection 271
Eye search regions 272
Step 3: Collecting faces and learning from them 281
Collecting preprocessed faces for training 283
Training the face recognition system from collected faces 285
Viewing the learned knowledge 287
Average face 289
Eigenvalues, Eigenfaces, and Fisherfaces 290
Step 4: Face recognition 292
Face identication: Recognizing people from their face 292
Face verication: Validating that it is the claimed person 292
Finishing touches: Saving and loading les 295
Finishing touches: Making a nice and interactive GUI 295
Drawing the GUI elements 297
Checking and handling mouse clicks 306
Summary 308
References 309
Index 311
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Preface
Mastering OpenCV with Practical Computer Vision Projects contains nine chapters, where
each chapter is a tutorial for an entire project from start to nish, based on OpenCV's
C++ interface including full source code. The author of each chapter was chosen for
their well-regarded online contributions to the OpenCV community on that topic,
and the book was reviewed by one of the main OpenCV developers. Rather than
explaining the basics of OpenCV functions, this is the rst book that shows how
to apply OpenCV to solve whole problems, including several 3D camera projects
(augmented reality, 3D Structure from Motion, Kinect interaction) and several facial
analysis projects (such as, skin detection, simple face and eye detection, complex facial
feature tracking, 3D head orientation estimation, and face recognition), therefore it
makes a great companion to existing OpenCV books.
What this book covers
Chapter 1, Cartoonier and Skin Changer for Android, contains a complete tutorial and
source code for both a desktop application and an Android app that automatically
generates a cartoon or painting from a real camera image, with several possible types
of cartoons including a skin color changer.
Chapter 2, Marker-based Augmented Reality on iPhone or iPad, contains a complete
tutorial on how to build a marker-based augmented reality (AR) application for
iPad and iPhone devices with an explanation of each step and source code.
Chapter 3, Marker-less Augmented Reality, contains a complete tutorial on how to
develop a marker-less augmented reality desktop application with an explanation
of what marker-less AR is and source code.
Chapter 4, Exploring Structure from Motion Using OpenCV, contains an introduction
to Structure from Motion (SfM) via an implementation of SfM concepts in OpenCV.
The reader will learn how to reconstruct 3D geometry from multiple 2D images and
estimate camera positions.
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