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  • Book Overview & Buying OpenCV Essentials
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OpenCV Essentials

OpenCV Essentials

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

OpenCV Essentials

4.3 (13)
By: Oscar Deniz 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

Contours and connected components

Contour extraction operations can be considered halfway between feature extraction and segmentation, since a binary image is produced in which image contours are separated from other homogeneous regions. Contours will typically correspond to object boundaries.

While a number of simple methods detect edges in images (for example, the Sobel and Laplace filters), the Canny method is a robust algorithm for doing this.

Note

This method uses two thresholds to decide whether a pixel is an edge. In what is called a hysteresis procedure, a lower and an upper threshold are used (see http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html). Since OpenCV already includes a good example of the Canny edge detector (in [opencv_source_code]/samples/cpp/edge.cpp), we do not include one here (but see the following floodFill example). Instead, we will go on to describe other highly useful functions based on detected edges.

To detect straight lines...

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OpenCV Essentials
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