Index
A
- ANNs (artificial neural networks) / Introducing machine learning concepts
- application
- creating, for AOI / Creating an application for AOI
- automatic object inspection classification example
B
- background subtraction
- about / Understanding background subtraction
- naive background subtraction / Naive background subtraction
- basic CMake configuration files
- about / Basic CMake configuration files
- basic data persistence
- basic graphical user interface
- with OpenCV / A basic graphical user interface with OpenCV
- basic matrix operations
- about / Basic matrix operations
- basic object types
- about / Other basic object types
- vec object type / The vec object type
- Scalar object type / The Scalar object type
- Point object type / The Point object type
- Size object type / The Size object type
- Rect object type / The Rect object type
- RotatedRect object type / RotatedRect object type
- Black Hat transform
- about / Black Top-Hat transform
- blurring / The scene detection problem
- buttons
- adding, to user interface / Adding buttons to a user interface
C
- cameras
- reading / Reading videos and cameras
- cartoonize effect
- about / The cartoonize effect
- creating / The cartoonize effect
- classifiers / Text detection
- CMake
- reference / Mac OS X
- CMake script file
- generating / Generating a CMake script file
- complex script
- creating / Making the script more complex
- Computer Vision applications
- machine learning workflow / Computer Vision and the machine learning workflow
- connected component algorithm
- connectedComponents function
- about / The connected component algorithm
- image parameter / The connected component algorithm
- labels parameter / The connected component algorithm
- connectivity parameter / The connected component algorithm
- type parameter / The connected component algorithm
- connectedComponentsWithStats function
- about / The connected component algorithm
- Stats parameter / The connected component algorithm
- CC_STAT_LEFT parameter / The connected component algorithm
- CC_STAT_TO parameter / The connected component algorithm
- CC_STAT_WIDTH parameter / The connected component algorithm
- CC_STAT_HEIGHT parameter / The connected component algorithm
- CC_STAT_AREA parameter / The connected component algorithm
- Centroids parameter / The connected component algorithm
- Continuously Adaptive Meanshift / Building an interactive object tracker
- corner detection
- corner point
- CVPR (Computer Vision and Pattern Recognition) / How the text API works
- CV_GUI_EXPANDED flag
- CV_GUI_NORMAL flag
D
- datapath parameter / Creating a OCR function, Text recognition
- data storage
- file storage, writing to / Writing to a file storage
- decision tree / Introducing machine learning concepts
- dependencies
- managing / Managing dependencies
- dilation
- about / Thickening the shapes
- drawER function / Text extraction
- Dynamic Link Libraries (DLLs) / Windows
E
- extremal regions
- about / Extremal regions
- filtering / Extremal region filtering
F
- facemask
- overlaying, in live video / Overlaying a facemask in a live video
- code / What happened in the code?
- feature-based tracking
- about / Feature-based tracking
- Lucas-Kanade method / The Lucas-Kanade method
- Farneback algorithm / The Farneback algorithm
- feature extraction
- about / Feature extraction
- performing / Feature extraction
- SVM model, training / Training an SVM model
- input image prediction / Input image prediction
- feature points
- findContours algorithm
- about / The findContours algorithm
- flags / Text extraction
- frame differencing
- about / Frame differencing
- working / How well does it work?
G
- getRectSubPix function, arguments
- IMAGE / Text extraction and skew adjustment
- SIZE / Text extraction and skew adjustment
- CENTER / Text extraction and skew adjustment
- PATCH / Text extraction and skew adjustment
- PATCH_TYPE / Text extraction and skew adjustment
- getRotationMatrix2D OpenCV function
- CENTER parameter / Text extraction and skew adjustment
- ANGLE parameter / Text extraction and skew adjustment
- SCALE parameter / Text extraction and skew adjustment
- GitHub
- Good Features To Track
- about / Shi-Tomasi Corner Detector
- graphical user interface
- with QT / The graphical user interface with QT
- creating / Creating the Graphical User Interface
H
- Haar cascades
- about / Understanding Haar cascades
- features / Understanding Haar cascades
- Harris corner detector
- for detecting points / Detecting points using the Harris corner detector
- HDRI (High Dynamic Range Imaging) / Images and matrices
- Hierarchical Method for Oriented Text / Extremal region filtering
- histogram
- about / Drawing a histogram
- drawing / Drawing a histogram
- HSV (Hue Saturation Value) / Tracking objects of a specific color
- human visual system
- about / Understanding the human visual system
- image content, understanding / How do humans understand image content?
I
- illumination and shadows / The scene detection problem
- image color equalization
- about / Image color equalization
- image parameter / Text extraction
- images
- about / Images and matrices
- reading/writing / Reading/writing images
- imread function
- about / Reading/writing images
- imwrite function / Reading/writing images
- input channel / Text detection
- input image
- preprocessing / Preprocessing the input image
- noise removal / Noise removal
- background, removing with light pattern for segmentation / Removing the background using the light pattern for segmentation
- thresholding operation / The thresholding operation
- input image, segmenting
- about / Segmenting our input image
- connected component algorithm / The connected component algorithm
- findContours algorithm / The findContours algorithm
- integral images
- about / What are integral images?
- interactive object tracker
- building / Building an interactive object tracker
- interest point
- interest points
K
- K-nearest neighbors / Introducing machine learning concepts
L
- language parameter / Creating a OCR function, Text recognition
- library
- creating / Creating a library
- Linux
- OpenCV, installing on / Linux
- loDiff parameter / Text extraction
- lomography effect
- creating / Lomography effect
- Look up Table (LUT)
- about / Lomography effect
M
- Mac
- Tesseract, installing / Installing Tesseract on Mac
- machine learning
- concepts / Introducing machine learning concepts
- about / Introducing machine learning concepts
- machine learning algorithm
- classification / Introducing machine learning concepts
- regression / Introducing machine learning concepts
- clustering / Introducing machine learning concepts
- density estimation / Introducing machine learning concepts
- machine learning class hierarchy
- StatModel class / Introducing machine learning concepts
- machines
- image content, challenges / Why is it difficult for machines to understand image content?
- Mac OS X
- OpenCV, installing on / Mac OS X
- mask parameter / Text extraction
- matrices
- about / Images and matrices
- maximally stable extremal regions (MSERs) / Text segmentation
- Mixture of Gaussians approach
- about / The Mixture of Gaussians approach
- code / What happened in the code?
- MOG2
- morphological image processing
- about / Morphological image processing
- underlying principle / What's the underlying principle?
- morphological operators
- about / Other morphological operators
- morphological opening / Morphological opening
- morphological closing / Morphological closing
- boundary, drawing / Drawing the boundary
- Top Hat transform / White Top-Hat transform
- Black Hat transform / Black Top-Hat transform
- mouse events and slider events
- adding, to interfaces / Adding slider and mouse events to our interfaces
- MSERs (Maximally Stable Extremal Regions) / Extremal regions
N
- naive background subtraction
- about / Naive background subtraction
- working / Does it work well?
- negative samples / Understanding Haar cascades
- Neural Network approach / Introducing machine learning concepts
- newVal parameter / Text extraction
- nose
- detecting, framework used / Tracking your nose, mouth, and ears
O
- objects
- isolating, in scene / Isolating objects in a scene
- objects of specific color
- tracking / Tracking objects of a specific color
- OCR
- text preprocessing and segmentation / Introducing optical character recognition
- text identification / Introducing optical character recognition
- preprocessing step / The preprocessing step
- text segmentation / Text segmentation
- OcrEngineMode
- about / Creating a OCR function
- OEM_TESSERACT_ONLY / Creating a OCR function
- OEM_CUBE_ONLY / Creating a OCR function
- OEM_TESSERACT_CUBE_COMBINED / Creating a OCR function
- OEM_DEFAULT / Creating a OCR function
- OCR function
- creating / Creating a OCR function
- output, sending to file / Sending the output to a file
- oem parameter / Text recognition
- OpenCV
- about / What can you do with OpenCV?
- features / What can you do with OpenCV?
- in-built data structures / In-built data structures and input/output
- input/output / In-built data structures and input/output
- image processing operations / Image processing operations
- GUI, building / Building GUI
- video analysis / Video analysis
- 3D reconstruction / 3D reconstruction
- feature extraction / Feature extraction
- object detection / Object detection
- machine learning / Machine learning
- computational photography / Computational photography
- shape analysis / Shape analysis
- optical flow algorithms / Optical flow algorithms
- face recognition / Face and object recognition
- object recognition / Face and object recognition
- surface matching / Surface matching
- text detection and recognition / Text detection and recognition
- installing / Installing OpenCV
- URL / Windows
- installing, on Windows / Windows
- installing, on Mac OS X / Mac OS X
- installing, on Linux / Linux
- OpenCV calcHist function
- about / Drawing a histogram
- OpenCV document page, machine learning
- reference / Introducing machine learning concepts
- OpenCV user interface
- OpenGL support
- about / OpenGL support
P
- parameters, mat object
- data / Creating a OCR function
- width / Creating a OCR function
- height / Creating a OCR function
- bytes_per_pixel / Creating a OCR function
- bytes_per_line / Creating a OCR function
- Path Editor
- reference / Windows
- Point object type
- about / The Point object type
- positive samples / Understanding Haar cascades
- predict method, StatModel class
- about / Introducing machine learning concepts
- samples parameter / Introducing machine learning concepts
- results parameter / Introducing machine learning concepts
- flags parameter / Introducing machine learning concepts
- preprocessing step, OCR
- about / The preprocessing step
- image, thresholding / Thresholding the image
- Prune Exhaustive Search / Extremal region filtering
- psmode parameter / Text recognition
R
- Rect object type
- about / The Rect object type
- rect parameter / Text extraction
- regions / Text detection
- reinforcement learning
- ROI (region of interest)
- about / The Rect object type
- RotatedRect object type
- about / RotatedRect object type
S
- Scalar object type
- about / The Scalar object type
- scene
- objects, isolating / Isolating objects in a scene
- seedPoint parameter / Text extraction
- segmentation modes
- PSM_OSD_ONLY / Creating a OCR function
- PSM_AUTO_OSD / Creating a OCR function
- PSM_AUTO_ONLY / Creating a OCR function
- PSM_AUTO / Creating a OCR function
- PSM_SINGLE_COLUMN / Creating a OCR function
- PSM_SINGLE_BLOCK_VERT_TEXT / Creating a OCR function
- PSM_SINGLE_BLOCK / Creating a OCR function
- PSM_SINGLE_LINE / Creating a OCR function
- PSM_SINGLE_WORD / Creating a OCR function
- PSM_SINGLE_WORD_CIRCLE / Creating a OCR function
- PSM_SINGLE_CHAR / Creating a OCR function
- shapes
- slimming / Slimming the shapes
- thickening / Thickening the shapes
- showHistoCallback function
- about / Drawing a histogram
- Single Linkage Clustering / Text detection
- Size object type
- about / The Size object type
- StatModel class
- train method / Introducing machine learning concepts
- predict method / Introducing machine learning concepts
- isTrained() method / Introducing machine learning concepts
- isClassifier() method / Introducing machine learning concepts
- getVarCount() method / Introducing machine learning concepts
- save(const string& filename) method / Introducing machine learning concepts
- Ptr<_Tp> load(const string& filename) method / Introducing machine learning concepts
- calcError(const Ptr<TrainData>& data, bool test, OutputArray resp) method / Introducing machine learning concepts
- sunglasses
- overlaying, in live video / Get your sunglasses on
- code / Looking inside the code
- supervised learning
- SVM (support vector machines) / Introducing machine learning concepts
T
- Tesseract
- about / Installing Tesseract OCR on your operating system
- installing / Installing Tesseract OCR on your operating system
- installing, on Windows / Installing Tesseract on Windows
- reference, for installer / Installing Tesseract on Windows
- setting up, in Visual Studio / Setting up Tesseract in Visual Studio
- import path, setting / Setting the import and library paths
- library path, setting / Setting the import and library paths
- linker, configuring / Configuring the linker
- libraries, adding to windows path / Adding the libraries to the windows path
- installing, on Mac / Installing Tesseract on Mac
- Tesseract OCR
- installing, on operating system / Installing Tesseract OCR on your operating system
- Tesseract OCR library
- using / Using Tesseract OCR library
- OCR function, creating / Creating a OCR function
- text API
- about / How the text API works
- working / How the text API works
- scene detection problem / The scene detection problem
- extremal regions / Extremal regions
- extremal region filtering / Extremal region filtering
- using / Using the text API
- text detection / Text detection
- text extraction / Text extraction
- text recognition / Text recognition
- text segmentation, OCR
- about / Text segmentation
- connected areas, creating / Creating connected areas
- paragraph blocks, identifying / Identifying paragraph blocks
- text extraction / Text extraction and skew adjustment
- adjustment, skewing / Text extraction and skew adjustment
- Top Hat transform
- about / White Top-Hat transform
- train method, StatModel class
- about / Introducing machine learning concepts
- trainData parameter / Introducing machine learning concepts
- samples parameter / Introducing machine learning concepts
- layout parameter / Introducing machine learning concepts
- responses parameter / Introducing machine learning concepts
- p parameter / Introducing machine learning concepts
- flags parameter / Introducing machine learning concepts
- tri-dimensionality / The scene detection problem
U
- unsupervised learning
- upDiff parameter / Text extraction
V
- variety / The scene detection problem
- vec object type
- about / The vec object type
- videos
- reading / Reading videos and cameras
W
- warpAffine function
- warpAffine function, arguments
- SRC / Text extraction and skew adjustment
- DST / Text extraction and skew adjustment
- M / Text extraction and skew adjustment
- SIZE / Text extraction and skew adjustment
- FLAGS / Text extraction and skew adjustment
- BORDER / Text extraction and skew adjustment
- BORDER VALUE / Text extraction and skew adjustment
- whitelist parameter / Text recognition
- Windows
- OpenCV, installing on / Windows
- Tesseract, installing / Installing Tesseract on Windows
- WINDOW_AUTOSIZE flag
- WINDOW_FREERATIO flag
- WINDOW_KEEPRATIO flag
- WINDOW_NORMAL flag
- WINDOW_OPENGL flag