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  • Book Overview & Buying OpenCV Essentials
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OpenCV Essentials

OpenCV Essentials

By : Suarez
4.3 (13)
Buy this Book
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OpenCV Essentials

OpenCV Essentials

4.3 (13)
By: Suarez
Buy this Book

Overview of this book

This book is intended for C++ developers who want to learn how to implement the main techniques of OpenCV and get started with it quickly. Working experience with computer vision / image processing is expected.
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started
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1. Getting Started
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Setting up OpenCV
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API concepts and basic datatypes
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Our first program – reading and writing images and videos
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Reading and playing a video file
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Live input from a camera
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Summary
2
2. Something We Look At – Graphical User Interfaces
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2. Something We Look At – Graphical User Interfaces
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Using OpenCV's highgui module
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Text and drawing
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Selecting regions
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Using Qt-based functions
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Summary
3
3. First Things First – Image Processing
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3. First Things First – Image Processing
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Pixel-level access and common operations
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Image histogram
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Histogram equalization
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Brightness and contrast modeling
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Histogram matching and LUT
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Conversion from RGB to other color spaces
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Filtering with the retina model
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Arithmetic and geometrical transforms
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Summary
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What else?
4
4. What's in the Image? Segmentation
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4. What's in the Image? Segmentation
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Thresholding
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Contours and connected components
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Flood fill
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Watershed segmentation
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GrabCut
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Summary
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What else?
5
5. Focusing on the Interesting 2D Features
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5. Focusing on the Interesting 2D Features
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Interest points
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Feature detectors
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Feature descriptor extractors
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Descriptor matchers
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Summary
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What else?
6
6. Where's Wally? Object Detection
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6. Where's Wally? Object Detection
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Object detection
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Detecting objects with OpenCV
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Cascades are beautiful
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Latent SVM
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Scene text detection
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Summary
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What else?
7
7. What Is He Doing? Motion
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7. What Is He Doing? Motion
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Motion history
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Reading video sequences
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The Lucas-Kanade optical flow
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The Gunnar-Farneback optical flow
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The Mean-Shift tracker
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The CamShift tracker
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The Motion templates
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The Background subtraction technique
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Image alignment
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Summary
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What else?
8
8. Advanced Topics
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8. Advanced Topics
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Machine learning
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The KNN classifier
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The Random Forest classifier
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SVM for classification
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What about GPUs?
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Setting up OpenCV with CUDA
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Our first GPU-based program
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Going real time
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Summary
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What else?
9
Index
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Index

Watershed segmentation

Watershed is a segmentation method that is known for its efficiency. The method essentially starts from user-specified starting (seed) points from which regions grow. Assuming that good starting seeds can be provided, the resulting segmentations are useful for many purposes.

Note

For more details and examples about the watershed transform for image segmentation, see http://cmm.ensmp.fr/~beucher/wtshed.html.

The function watershed(InputArray image, InputOutputArray markers) accepts a 3-channel input image and an image called markers with the seeds. The latter has to be a 32-bit single-channel image. Seeds may be specified in markers as connected components with positive values (0 cannot be used as a value for seeds). As an output argument, each pixel in markers will be set to a value of the seed components or -1 at boundaries between the regions. OpenCV includes a watershed example ([opencv_source_code]/samples/cpp/watershed.cpp) in which the user has to draw the seed...

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OpenCV Essentials
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