Mastering OpenCV 3: get hands-on with practical computer vision using OpenCV 3
Practical Computer Vision ProjectsAbout This Book* Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3* Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications* P...
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Birmingham; Mumbai
Packt Publishing
2017
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Practical Computer Vision ProjectsAbout This Book* Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3* Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications* Project-based approach with each chapter being a complete tutorial, showing you how to apply OpenCV to solve complete problemsWho This Book Is ForThis book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book. What You Will Learn* Execute basic image processing operations and cartoonify an image* Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text* Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video* Use OpenCV 3's new 3D visualization framework to illustrate the 3D scene geometry* Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks* Train and predict pattern-recognition algorithms to decide whether an image is a number plate* Use POSIT for the six degrees of freedom head pose* Train a face recognition database using deep learning and recognize faces from that databaseIn DetailAs we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision. This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You'll learn how to make AI that can remember and use neural networks to help your applications learn. By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3. Style and approachThis book takes a project-based approach and helps you learn about the new features by putting them to work by implementing them in your own projects |
| Beschreibung: | iv, 233 Seiten Illustrationen, Digramme |
| ISBN: | 9781786467171 1786467178 |
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| 245 | 1 | 0 | |a Mastering OpenCV 3 |b get hands-on with practical computer vision using OpenCV 3 |c Daniel Lélis Baggio ; Shervin Emami ; David Millá Escrivá : Khvedchenia Ievgen ; Jason Saragihi ; Roy Shilkrot |
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| 505 | 8 | |a Cover -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: 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 -- [Reducing the random pepper noise from the sketch image] -- Reducing the random pepper noise from the sketch image -- Porting from desktop to embedded -- Equipment 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 to run full screen -- 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 computer -- Customizing your embedded system! -- Summary -- Chapter 2: 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 -- Chapter 3: Number Plate Recognition using SVM and Neural Network -- Introduction to ANPR -- ANPR algorithm | |
| 505 | 8 | |a Plate detection -- Segmentation -- Classification -- Plate recognition -- OCR segmentation -- Feature extraction -- OCR classification -- Evaluation -- Summary -- Chapter 4: 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 -- Chapter 5: 3D Head Pose Estimation Using AAM and POSIT -- Active Appearance Models overview -- [Overview of the chapter algorithms] -- [Overview of the chapter algorithms] -- Overview of the chapter algorithms -- 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 -- Chapter 6: 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 | |
| 505 | 8 | |a 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 -- Index | |
| 520 | 3 | |a Practical Computer Vision ProjectsAbout This Book* Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3* Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications* Project-based approach with each chapter being a complete tutorial, showing you how to apply OpenCV to solve complete problemsWho This Book Is ForThis book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book. | |
| 520 | 3 | |a What You Will Learn* Execute basic image processing operations and cartoonify an image* Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text* Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video* Use OpenCV 3's new 3D visualization framework to illustrate the 3D scene geometry* Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks* Train and predict pattern-recognition algorithms to decide whether an image is a number plate* Use POSIT for the six degrees of freedom head pose* Train a face recognition database using deep learning and recognize faces from that databaseIn DetailAs we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. | |
| 520 | 3 | |a This is all powered by Computer Vision. This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You'll learn how to make AI that can remember and use neural networks to help your applications learn. By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3. Style and approachThis book takes a project-based approach and helps you learn about the new features by putting them to work by implementing them in your own projects | |
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Datensatz im Suchindex
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| adam_text | Table of Contents Preface______________________________________________________________չ Chapter 1: Cartoonifier and Skin Changer for Raspberry Pi______________ 7 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 embedded Equipment 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 to run full screen 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 computer Customizing your embedded system! g 11 12 13 15 16 17 17 20 25 27 29 31 34 37 38 39 39 40 40 41 42 44 46 Summary 47 Chapter 2: Exploring Structure from Motion Using OpenCV_____________ 49 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 51 52 53 56 61 63 66 71 74
Summary References Chapter 3: Number Plate Recognition using SVIVI and Neural Network Introduction to ANPR ANPR algorithm Plate detection Segmentation Classification Plate recognition OCR segmentation Feature extraction OCR classification Evaluation Summary 75 76 n 77 80 82 83 90 ցյ 94 95 97 102 105 Chapter 4: Non-Rigid Face Tracking____________________________________107 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 109 109 110 112 112 116 117 118 120 124 127 129 132 134 134 138 139 142 144 148 149 151 152 153
References 153 Chapter 5: 3D Head Pose Estimation Using AAM and POSIT____________155 Active Appearance Models overview Overview of the chapter algorithms 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 156 157 158 160 164 166 168 16Ց 172 173 175 176 178 179 Chapter 6: Face Recognition Using Eigenfaces or Fisherfaces_________ ışı 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 181 183 184 185 186 186 186 187 187 188 190 190 191 195 197 199 200 201 203 205 207 209 210 212 Face identification - recognizing people from their face [iii] 213
Face verification - validating that it is the claimed person 213 Finishing touches - saving and loading files Finishing touches - making a nice and interactive GUI 216 Drawing the GUI elements Startup mode Detection mode Collection mode Training mode Recognition mode Checking and handling mouse clicks Summary References 216 217 220 220 222 224 225 226 229 229 Index 231 [iv]
|
| adam_txt |
Table of Contents Preface_չ Chapter 1: Cartoonifier and Skin Changer for Raspberry Pi_ 7 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 embedded Equipment 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 to run full screen 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 computer Customizing your embedded system! g 11 12 13 15 16 17 17 20 25 27 29 31 34 37 38 39 39 40 40 41 42 44 46 Summary 47 Chapter 2: Exploring Structure from Motion Using OpenCV_ 49 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 51 52 53 56 61 63 66 71 74
Summary References Chapter 3: Number Plate Recognition using SVIVI and Neural Network Introduction to ANPR ANPR algorithm Plate detection Segmentation Classification Plate recognition OCR segmentation Feature extraction OCR classification Evaluation Summary 75 76 n 77 80 82 83 90 ցյ 94 95 97 102 105 Chapter 4: Non-Rigid Face Tracking_107 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 109 109 110 112 112 116 117 118 120 124 127 129 132 134 134 138 139 142 144 148 149 151 152 153
References 153 Chapter 5: 3D Head Pose Estimation Using AAM and POSIT_155 Active Appearance Models overview Overview of the chapter algorithms 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 156 157 158 160 164 166 168 16Ց 172 173 175 176 178 179 Chapter 6: Face Recognition Using Eigenfaces or Fisherfaces_ ışı 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 181 183 184 185 186 186 186 187 187 188 190 190 191 195 197 199 200 201 203 205 207 209 210 212 Face identification - recognizing people from their face [iii] 213
Face verification - validating that it is the claimed person 213 Finishing touches - saving and loading files Finishing touches - making a nice and interactive GUI 216 Drawing the GUI elements Startup mode Detection mode Collection mode Training mode Recognition mode Checking and handling mouse clicks Summary References 216 217 220 220 222 224 225 226 229 229 Index 231 [iv] |
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| contents | Cover -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: 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 -- [Reducing the random pepper noise from the sketch image] -- Reducing the random pepper noise from the sketch image -- Porting from desktop to embedded -- Equipment 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 to run full screen -- 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 computer -- Customizing your embedded system! -- Summary -- Chapter 2: 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 -- Chapter 3: 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 -- Chapter 4: 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 -- Chapter 5: 3D Head Pose Estimation Using AAM and POSIT -- Active Appearance Models overview -- [Overview of the chapter algorithms] -- [Overview of the chapter algorithms] -- Overview of the chapter algorithms -- 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 -- Chapter 6: 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 -- Index |
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| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>09414nam a2200541 c 4500</leader><controlfield tag="001">BV047501952</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20211118 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">211007s2017 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781786467171</subfield><subfield code="9">978-1-78646-717-1</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1786467178</subfield><subfield code="9">1-78646-717-8</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1286860574</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047501952</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-739</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 331</subfield><subfield code="0">(DE-625)143664:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Mastering OpenCV 3</subfield><subfield code="b">get hands-on with practical computer vision using OpenCV 3</subfield><subfield code="c">Daniel Lélis Baggio ; Shervin Emami ; David Millá Escrivá : Khvedchenia Ievgen ; Jason Saragihi ; Roy Shilkrot</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham; Mumbai</subfield><subfield code="b">Packt Publishing</subfield><subfield code="c">2017</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">iv, 233 Seiten</subfield><subfield code="b">Illustrationen, Digramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: 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 -- [Reducing the random pepper noise from the sketch image] -- Reducing the random pepper noise from the sketch image -- Porting from desktop to embedded -- Equipment 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 to run full screen -- 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 computer -- Customizing your embedded system! -- Summary -- Chapter 2: 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 -- Chapter 3: Number Plate Recognition using SVM and Neural Network -- Introduction to ANPR -- ANPR algorithm</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Plate detection -- Segmentation -- Classification -- Plate recognition -- OCR segmentation -- Feature extraction -- OCR classification -- Evaluation -- Summary -- Chapter 4: 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 -- Chapter 5: 3D Head Pose Estimation Using AAM and POSIT -- Active Appearance Models overview -- [Overview of the chapter algorithms] -- [Overview of the chapter algorithms] -- Overview of the chapter algorithms -- 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 -- Chapter 6: 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 -- Index</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Practical Computer Vision ProjectsAbout This Book* Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3* Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications* Project-based approach with each chapter being a complete tutorial, showing you how to apply OpenCV to solve complete problemsWho This Book Is ForThis book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. 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| id | DE-604.BV047501952 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:19:11Z |
| indexdate | 2024-07-10T09:13:51Z |
| institution | BVB |
| isbn | 9781786467171 1786467178 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032902990 |
| oclc_num | 1286860574 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | iv, 233 Seiten Illustrationen, Digramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt Publishing |
| record_format | marc |
| spelling | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 Daniel Lélis Baggio ; Shervin Emami ; David Millá Escrivá : Khvedchenia Ievgen ; Jason Saragihi ; Roy Shilkrot Second edition Birmingham; Mumbai Packt Publishing 2017 iv, 233 Seiten Illustrationen, Digramme txt rdacontent n rdamedia nc rdacarrier Cover -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: 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 -- [Reducing the random pepper noise from the sketch image] -- Reducing the random pepper noise from the sketch image -- Porting from desktop to embedded -- Equipment 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 to run full screen -- 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 computer -- Customizing your embedded system! -- Summary -- Chapter 2: 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 -- Chapter 3: 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 -- Chapter 4: 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 -- Chapter 5: 3D Head Pose Estimation Using AAM and POSIT -- Active Appearance Models overview -- [Overview of the chapter algorithms] -- [Overview of the chapter algorithms] -- Overview of the chapter algorithms -- 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 -- Chapter 6: 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 -- Index Practical Computer Vision ProjectsAbout This Book* Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3* Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications* Project-based approach with each chapter being a complete tutorial, showing you how to apply OpenCV to solve complete problemsWho This Book Is ForThis book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book. What You Will Learn* Execute basic image processing operations and cartoonify an image* Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text* Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video* Use OpenCV 3's new 3D visualization framework to illustrate the 3D scene geometry* Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks* Train and predict pattern-recognition algorithms to decide whether an image is a number plate* Use POSIT for the six degrees of freedom head pose* Train a face recognition database using deep learning and recognize faces from that databaseIn DetailAs we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision. This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You'll learn how to make AI that can remember and use neural networks to help your applications learn. By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3. Style and approachThis book takes a project-based approach and helps you learn about the new features by putting them to work by implementing them in your own projects OpenCV (DE-588)1038770092 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Computer vision OpenCV (DE-588)1038770092 s Maschinelles Sehen (DE-588)4129594-8 s DE-604 Baggio, Daniel Lélis Sonstige (DE-588)1035797607 oth Emami, Shervin Sonstige (DE-588)1035798476 oth Escrivá, David Millán Sonstige (DE-588)109759498X oth Ievgen, Khvedchenia ca. 20./21. Jh. Sonstige (DE-588)1246023652 oth Saragih, Jason ca. 20./21. Jh. Sonstige (DE-588)1246024055 oth Shilkrot, Roy ca. 20.21. Jh. Sonstige (DE-588)124602456X oth Print version Baggio, Daniel Lelis Mastering OpenCV 3 - Second Edition Birmingham : Packt Publishing, ©2017 Erscheint auch als Online-Ausgabe 1786467178 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032902990&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 Cover -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: 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 -- [Reducing the random pepper noise from the sketch image] -- Reducing the random pepper noise from the sketch image -- Porting from desktop to embedded -- Equipment 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 to run full screen -- 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 computer -- Customizing your embedded system! -- Summary -- Chapter 2: 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 -- Chapter 3: 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 -- Chapter 4: 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 -- Chapter 5: 3D Head Pose Estimation Using AAM and POSIT -- Active Appearance Models overview -- [Overview of the chapter algorithms] -- [Overview of the chapter algorithms] -- Overview of the chapter algorithms -- 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 -- Chapter 6: 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 -- Index OpenCV (DE-588)1038770092 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| subject_GND | (DE-588)1038770092 (DE-588)4129594-8 |
| title | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 |
| title_auth | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 |
| title_exact_search | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 |
| title_exact_search_txtP | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 |
| title_full | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 Daniel Lélis Baggio ; Shervin Emami ; David Millá Escrivá : Khvedchenia Ievgen ; Jason Saragihi ; Roy Shilkrot |
| title_fullStr | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 Daniel Lélis Baggio ; Shervin Emami ; David Millá Escrivá : Khvedchenia Ievgen ; Jason Saragihi ; Roy Shilkrot |
| title_full_unstemmed | Mastering OpenCV 3 get hands-on with practical computer vision using OpenCV 3 Daniel Lélis Baggio ; Shervin Emami ; David Millá Escrivá : Khvedchenia Ievgen ; Jason Saragihi ; Roy Shilkrot |
| title_short | Mastering OpenCV 3 |
| title_sort | mastering opencv 3 get hands on with practical computer vision using opencv 3 |
| title_sub | get hands-on with practical computer vision using OpenCV 3 |
| topic | OpenCV (DE-588)1038770092 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | OpenCV Maschinelles Sehen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032902990&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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