Hands-On algorithms for computer vision: learn how to use the best and most practical computer vision algorithms using OpenCV
The field of Computer Vision has seen advancements in terms of processing power and performance. Many algorithms are introduced to perform Computer Vision tasks efficiently. This book is a starting point for anyone interested in this field and wants to dig deeper into the most practical algorithms u...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt Publishing
2018
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| Schlagworte: | |
| Zusammenfassung: | The field of Computer Vision has seen advancements in terms of processing power and performance. Many algorithms are introduced to perform Computer Vision tasks efficiently. This book is a starting point for anyone interested in this field and wants to dig deeper into the most practical algorithms used by professional Computer Vision developers Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Preface -- Table of Contents -- Chapter 1: Introduction to Computer Vision -- Technical requirements -- Understanding computer vision -- Learning all about images -- Color spaces -- Input, process, and output -- Computer vision frameworks and libraries -- Summary -- Questions -- Chapter 2: Getting Started with OpenCV -- Technical requirements -- Introduction to OpenCV -- The Main modules in OpenCV -- Downloading and building/installing OpenCV -- Using OpenCV with C++ or Python -- Understanding the Mat class -- Constructing a Mat object -- Deleting a Mat object -- Accessing pixels -- Reading and writing images -- Reading and writing videos -- Accessing cameras -- Accessing RTSP and network feeds -- Mat-like classes -- Summary -- Questions -- Further reading -- Chapter 3: Array and Matrix Operations -- Technical requirements -- Operations contained in the Mat class -- Cloning a matrix -- Calculating the cross-product -- Extracting a diagonal -- Calculating the dot product -- Learning about the identity matrix -- Matrix inversion -- Element-wise matrix multiplication -- The ones and zeroes matrix -- Transposing a matrix -- Reshaping a Mat object -- Element-wise matrix operations -- Basic operations -- The addition operation -- Weighted addition -- The subtraction operation -- The multiplication and division operations -- Bitwise logical operations -- The comparison operations -- The mathematical operations -- Matrix and array-wise operations -- Making borders for extrapolation -- Flipping (mirroring) and rotating images -- Working with channels -- Mathematical functions -- Matrix inversion -- Mean and sum of elements -- Discrete Fourier transformation -- Generating random numbers -- The search and locate functions -- Locating non-zero elements Locating minimum and maximum elements -- Lookup table transformation -- Summary -- Questions -- Chapter 4: Drawing, Filtering, and Transformation -- Technical requirements -- Drawing on images -- Printing text on images -- Drawing shapes -- Filtering images -- Blurring/smoothening filters -- Morphological filters -- Derivative-based filters -- Arbitrary filtering -- Transforming images -- Thresholding algorithms -- Color space and type conversion -- Geometric transformation -- Applying colormaps -- Summary -- Questions -- Chapter 5: Back-Projection and Histograms -- Technical requirements -- Understanding histograms -- Displaying histograms -- Back-projection of histograms -- Learning more about back-projections -- Comparing histograms -- Equalizing histograms -- Summary -- Questions -- Further reading -- Chapter 6: Video Analysis - Motion Detection and Tracking -- Technical requirements -- Processing videos -- Understanding the Mean Shift algorithm -- Using the Continuously Adaptive Mean (CAM) Shift -- Using the Kalman filter for tracking and noise reduction -- How to extract the background/foreground -- An example of background segmentation -- Summary -- Questions -- Chapter 7: Object Detection - Features and Descriptors -- Technical requirements -- Template matching for object detection -- Detecting corners and edges -- Learning the Harris corner-detection algorithm -- Edge-detection algorithms -- Contour calculation and analysis -- Detecting, descripting, and matching features -- Summary -- Questions -- Chapter 8: Machine Learning in Computer Vision -- Technical requirements -- Support vector machines -- Classifying images using SVM and HOG -- Training models with artificial neural networks -- The cascading classification algorithm -- Object detection using cascade classifiers -- Training cascade classifiers -- Creating samples |
| Beschreibung: | iv, 278 Seiten Illustrationen |
| ISBN: | 9781789130942 |
Internformat
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| 245 | 1 | 0 | |a Hands-On algorithms for computer vision |b learn how to use the best and most practical computer vision algorithms using OpenCV |c Amin Ahmadi Tazehkandi |
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| 264 | 1 | |a Birmingham ; Mumbai |b Packt Publishing |c 2018 | |
| 264 | 4 | |c © 2018 | |
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| 520 | 3 | |a The field of Computer Vision has seen advancements in terms of processing power and performance. Many algorithms are introduced to perform Computer Vision tasks efficiently. This book is a starting point for anyone interested in this field and wants to dig deeper into the most practical algorithms used by professional Computer Vision developers | |
| 520 | 3 | |a Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Preface -- Table of Contents -- Chapter 1: Introduction to Computer Vision -- Technical requirements -- Understanding computer vision -- Learning all about images -- Color spaces -- Input, process, and output -- Computer vision frameworks and libraries -- Summary -- Questions -- Chapter 2: Getting Started with OpenCV -- Technical requirements -- Introduction to OpenCV -- The Main modules in OpenCV -- Downloading and building/installing OpenCV -- Using OpenCV with C++ or Python -- Understanding the Mat class -- Constructing a Mat object -- Deleting a Mat object -- Accessing pixels -- Reading and writing images -- Reading and writing videos -- Accessing cameras -- Accessing RTSP and network feeds -- Mat-like classes -- Summary -- Questions -- Further reading -- Chapter 3: Array and Matrix Operations -- Technical requirements -- Operations contained in the Mat class -- Cloning a matrix -- Calculating the cross-product -- Extracting a diagonal -- Calculating the dot product -- Learning about the identity matrix -- Matrix inversion -- Element-wise matrix multiplication -- The ones and zeroes matrix -- Transposing a matrix -- Reshaping a Mat object -- Element-wise matrix operations -- Basic operations -- The addition operation -- Weighted addition -- The subtraction operation -- The multiplication and division operations -- Bitwise logical operations -- The comparison operations -- The mathematical operations -- Matrix and array-wise operations -- Making borders for extrapolation -- Flipping (mirroring) and rotating images -- Working with channels -- Mathematical functions -- Matrix inversion -- Mean and sum of elements -- Discrete Fourier transformation -- Generating random numbers -- The search and locate functions -- Locating non-zero elements | |
| 520 | 3 | |a Locating minimum and maximum elements -- Lookup table transformation -- Summary -- Questions -- Chapter 4: Drawing, Filtering, and Transformation -- Technical requirements -- Drawing on images -- Printing text on images -- Drawing shapes -- Filtering images -- Blurring/smoothening filters -- Morphological filters -- Derivative-based filters -- Arbitrary filtering -- Transforming images -- Thresholding algorithms -- Color space and type conversion -- Geometric transformation -- Applying colormaps -- Summary -- Questions -- Chapter 5: Back-Projection and Histograms -- Technical requirements -- Understanding histograms -- Displaying histograms -- Back-projection of histograms -- Learning more about back-projections -- Comparing histograms -- Equalizing histograms -- Summary -- Questions -- Further reading -- Chapter 6: Video Analysis - Motion Detection and Tracking -- Technical requirements -- Processing videos -- Understanding the Mean Shift algorithm -- Using the Continuously Adaptive Mean (CAM) Shift -- Using the Kalman filter for tracking and noise reduction -- How to extract the background/foreground -- An example of background segmentation -- Summary -- Questions -- Chapter 7: Object Detection - Features and Descriptors -- Technical requirements -- Template matching for object detection -- Detecting corners and edges -- Learning the Harris corner-detection algorithm -- Edge-detection algorithms -- Contour calculation and analysis -- Detecting, descripting, and matching features -- Summary -- Questions -- Chapter 8: Machine Learning in Computer Vision -- Technical requirements -- Support vector machines -- Classifying images using SVM and HOG -- Training models with artificial neural networks -- The cascading classification algorithm -- Object detection using cascade classifiers -- Training cascade classifiers -- Creating samples | |
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Datensatz im Suchindex
| _version_ | 1804180861559504896 |
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| any_adam_object | |
| author | Ahmadi Tazehkandi, Amin |
| author_GND | (DE-588)1152825852 |
| author_facet | Ahmadi Tazehkandi, Amin |
| author_role | aut |
| author_sort | Ahmadi Tazehkandi, Amin |
| author_variant | t a a ta taa |
| building | Verbundindex |
| bvnumber | BV046353452 |
| classification_rvk | ST 330 |
| ctrlnum | (OCoLC)1142690479 (DE-599)BVBBV046353452 |
| dewey-full | 006.37 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.37 |
| dewey-search | 006.37 |
| dewey-sort | 16.37 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV046353452 |
| illustrated | Illustrated |
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| institution | BVB |
| isbn | 9781789130942 |
| language | English |
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| spelling | Ahmadi Tazehkandi, Amin Verfasser (DE-588)1152825852 aut Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV Amin Ahmadi Tazehkandi algorithms for computer vision Birmingham ; Mumbai Packt Publishing 2018 © 2018 iv, 278 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier The field of Computer Vision has seen advancements in terms of processing power and performance. Many algorithms are introduced to perform Computer Vision tasks efficiently. This book is a starting point for anyone interested in this field and wants to dig deeper into the most practical algorithms used by professional Computer Vision developers Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Preface -- Table of Contents -- Chapter 1: Introduction to Computer Vision -- Technical requirements -- Understanding computer vision -- Learning all about images -- Color spaces -- Input, process, and output -- Computer vision frameworks and libraries -- Summary -- Questions -- Chapter 2: Getting Started with OpenCV -- Technical requirements -- Introduction to OpenCV -- The Main modules in OpenCV -- Downloading and building/installing OpenCV -- Using OpenCV with C++ or Python -- Understanding the Mat class -- Constructing a Mat object -- Deleting a Mat object -- Accessing pixels -- Reading and writing images -- Reading and writing videos -- Accessing cameras -- Accessing RTSP and network feeds -- Mat-like classes -- Summary -- Questions -- Further reading -- Chapter 3: Array and Matrix Operations -- Technical requirements -- Operations contained in the Mat class -- Cloning a matrix -- Calculating the cross-product -- Extracting a diagonal -- Calculating the dot product -- Learning about the identity matrix -- Matrix inversion -- Element-wise matrix multiplication -- The ones and zeroes matrix -- Transposing a matrix -- Reshaping a Mat object -- Element-wise matrix operations -- Basic operations -- The addition operation -- Weighted addition -- The subtraction operation -- The multiplication and division operations -- Bitwise logical operations -- The comparison operations -- The mathematical operations -- Matrix and array-wise operations -- Making borders for extrapolation -- Flipping (mirroring) and rotating images -- Working with channels -- Mathematical functions -- Matrix inversion -- Mean and sum of elements -- Discrete Fourier transformation -- Generating random numbers -- The search and locate functions -- Locating non-zero elements Locating minimum and maximum elements -- Lookup table transformation -- Summary -- Questions -- Chapter 4: Drawing, Filtering, and Transformation -- Technical requirements -- Drawing on images -- Printing text on images -- Drawing shapes -- Filtering images -- Blurring/smoothening filters -- Morphological filters -- Derivative-based filters -- Arbitrary filtering -- Transforming images -- Thresholding algorithms -- Color space and type conversion -- Geometric transformation -- Applying colormaps -- Summary -- Questions -- Chapter 5: Back-Projection and Histograms -- Technical requirements -- Understanding histograms -- Displaying histograms -- Back-projection of histograms -- Learning more about back-projections -- Comparing histograms -- Equalizing histograms -- Summary -- Questions -- Further reading -- Chapter 6: Video Analysis - Motion Detection and Tracking -- Technical requirements -- Processing videos -- Understanding the Mean Shift algorithm -- Using the Continuously Adaptive Mean (CAM) Shift -- Using the Kalman filter for tracking and noise reduction -- How to extract the background/foreground -- An example of background segmentation -- Summary -- Questions -- Chapter 7: Object Detection - Features and Descriptors -- Technical requirements -- Template matching for object detection -- Detecting corners and edges -- Learning the Harris corner-detection algorithm -- Edge-detection algorithms -- Contour calculation and analysis -- Detecting, descripting, and matching features -- Summary -- Questions -- Chapter 8: Machine Learning in Computer Vision -- Technical requirements -- Support vector machines -- Classifying images using SVM and HOG -- Training models with artificial neural networks -- The cascading classification algorithm -- Object detection using cascade classifiers -- Training cascade classifiers -- Creating samples OpenCV (DE-588)1038770092 gnd rswk-swf Computer vision Image processing Computer algorithms OpenCV (Computer program language) OpenCV (DE-588)1038770092 s DE-604 Erscheint auch als Online-Ausgabe 978-1-78913-094-2 |
| spellingShingle | Ahmadi Tazehkandi, Amin Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV OpenCV (DE-588)1038770092 gnd |
| subject_GND | (DE-588)1038770092 |
| title | Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV |
| title_alt | algorithms for computer vision |
| title_auth | Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV |
| title_exact_search | Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV |
| title_full | Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV Amin Ahmadi Tazehkandi |
| title_fullStr | Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV Amin Ahmadi Tazehkandi |
| title_full_unstemmed | Hands-On algorithms for computer vision learn how to use the best and most practical computer vision algorithms using OpenCV Amin Ahmadi Tazehkandi |
| title_short | Hands-On algorithms for computer vision |
| title_sort | hands on algorithms for computer vision learn how to use the best and most practical computer vision algorithms using opencv |
| title_sub | learn how to use the best and most practical computer vision algorithms using OpenCV |
| topic | OpenCV (DE-588)1038770092 gnd |
| topic_facet | OpenCV |
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