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Mastering OpenCV 3（Second Edition）
Daniel Lélis Baggio Shervin Emami David Millán Escrivá Khvedchenia Ievgen Jason Saragih Roy Shilkrot 著
更新時間：2021-07-02 23:29:29
開會員，本書免費讀 >
最新章節：
References
ThisbookisforthosewhohaveabasicknowledgeofOpenCVandarecompetentC++programmers.Youneedtohaveanunderstandingofsomeofthemoretheoretical/mathematicalconcepts,aswemovequitequicklythroughoutthebook.
最新章節
- References
- Summary
- Checking and handling mouse clicks
- Recognition mode
- Training mode
- Collection mode
品牌：中圖公司
上架時間：2021-07-02 18:53:43
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- References 更新時間：2021-07-02 23:29:29
- Summary
- Checking and handling mouse clicks
- Recognition mode
- Training mode
- Collection mode
- Detection mode
- Startup mode
- Drawing the GUI elements
- Finishing touches - making a nice and interactive GUI
- Finishing touches - saving and loading files
- Face verification - validating that it is the claimed person
- Face identification - recognizing people from their face
- Step 4 - face recognition
- Eigenvalues Eigenfaces and Fisherfaces
- Average face
- Viewing the learned knowledge
- Training the face recognition system from collected faces
- Collecting preprocessed faces for training
- Step 3 - Collecting faces and learning from them
- Elliptical mask
- Smoothing
- Separate histogram equalization for left and right sides
- Geometrical transformation
- Eye search regions
- Eye detection
- Step 2 - face preprocessing
- Detecting the face
- Histogram equalization
- Shrinking the camera image
- Grayscale color conversion
- Detecting an object using the Haar or LBP Classifier
- Accessing the webcam
- Loading a Haar or LBP detector for object or face detection
- Implementing face detection using OpenCV
- Step 1 - face detection
- Introduction to face recognition and face detection
- Face Recognition Using Eigenfaces or Fisherfaces
- References
- Summary
- Tracking from webcam or video file
- POSIT and head model
- Diving into POSIT
- POSIT
- AAM search and fitting
- Model Instantiation - playing with the AAM
- Triangle texture warping
- Triangulation
- Getting the feel of PCA
- Active Shape Models
- Active Appearance Models overview
- 3D Head Pose Estimation Using AAM and POSIT
- References
- Summary
- Generic versus person-specific models
- Training and visualization
- Face tracker implementation
- Face tracking
- Face detection and initialization
- Training and visualization
- Accounting for global geometric transformations
- Generative versus discriminative patch models
- Learning discriminative patch models
- Correlation-based patch models
- Facial feature detectors
- Training and visualization
- A combined local-global representation
- Linear shape models
- Procrustes analysis
- Geometrical constraints
- Pre-annotated data (the MUCT dataset)
- Annotation tool
- Training data types
- Data collection - image and video annotation
- Object-oriented design
- Utilities
- Overview
- Non-Rigid Face Tracking
- Summary
- Evaluation
- OCR classification
- Feature extraction
- OCR segmentation
- Plate recognition
- Classification
- Segmentation
- Plate detection
- ANPR algorithm
- Introduction to ANPR
- Number Plate Recognition using SVM and Neural Network
- References
- Summary
- Using the example code
- Refinement of the reconstruction
- Reconstruction from many views
- Reconstructing the scene
- Choosing the image pair to use first
- Finding camera matrices
- Point matching using rich feature descriptors
- Estimating the camera motion from a pair of images
- Structure from Motion concepts
- Exploring Structure from Motion Using OpenCV
- Summary
- Implementation of the skin color changer
- Showing the user where to put their face
- Skin detection algorithm
- Generating an alien mode using skin detection
- Generating an evil mode using edge filters
- Generating a color painting and a cartoon
- Generating a black and white sketch
- Main camera processing loop for a desktop app
- Accessing the webcam
- Cartoonifier and Skin Changer for Raspberry Pi
- Questions
- Piracy
- Errata
- Downloading the color images of this book
- Downloading the example code
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Customer Feedback
- www.PacktPub.com
- About the Reviewer
- About the Authors
- Credits
- 版權信息
- 封面
- 封面
- 版權信息
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- Cartoonifier and Skin Changer for Raspberry Pi
- 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
- Summary
- Exploring Structure from Motion Using OpenCV
- Structure from Motion concepts
- Estimating the camera motion from a pair of images
- Point matching using rich feature descriptors
- Finding camera matrices
- Choosing the image pair to use first
- Reconstructing the scene
- Reconstruction from many views
- Refinement of the reconstruction
- Using the example code
- Summary
- References
- Number Plate Recognition using SVM and Neural Network
- Introduction to ANPR
- ANPR algorithm
- Plate detection
- Segmentation
- Classification
- Plate recognition
- OCR segmentation
- Feature extraction
- OCR classification
- Evaluation
- Summary
- Non-Rigid Face Tracking
- Overview
- Utilities
- Object-oriented design
- Data collection - image and video annotation
- Training data types
- Annotation tool
- Pre-annotated data (the MUCT dataset)
- Geometrical constraints
- Procrustes analysis
- Linear shape models
- A combined local-global representation
- Training and visualization
- Facial feature detectors
- Correlation-based patch models
- Learning discriminative patch models
- Generative versus discriminative patch models
- Accounting for global geometric transformations
- Training and visualization
- Face detection and initialization
- Face tracking
- Face tracker implementation
- Training and visualization
- Generic versus person-specific models
- Summary
- References
- 3D Head Pose Estimation Using AAM and POSIT
- Active Appearance Models overview
- Active Shape Models
- Getting the feel of PCA
- Triangulation
- Triangle texture warping
- Model Instantiation - playing with the AAM
- AAM search and fitting
- POSIT
- Diving into POSIT
- POSIT and head model
- Tracking from webcam or video file
- Summary
- References
- Face Recognition Using Eigenfaces or Fisherfaces
- Introduction to face recognition and face detection
- Step 1 - face detection
- Implementing face detection using OpenCV
- Loading a Haar or LBP detector for object or face detection
- Accessing the webcam
- Detecting an object using the Haar or LBP Classifier
- Grayscale color conversion
- Shrinking the camera image
- Histogram equalization
- Detecting the face
- Step 2 - face preprocessing
- Eye detection
- Eye search regions
- Geometrical transformation
- Separate histogram equalization for left and right sides
- Smoothing
- Elliptical mask
- Step 3 - Collecting faces and learning from them
- Collecting preprocessed faces for training
- Training the face recognition system from collected faces
- Viewing the learned knowledge
- Average face
- Eigenvalues Eigenfaces and Fisherfaces
- Step 4 - face recognition
- Face identification - recognizing people from their face
- Face verification - validating that it is the claimed person
- Finishing touches - saving and loading files
- Finishing touches - making a nice and interactive GUI
- Drawing the GUI elements
- Startup mode
- Detection mode
- Collection mode
- Training mode
- Recognition mode
- Checking and handling mouse clicks
- Summary
- References 更新時間：2021-07-02 23:29:29
