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  • Book Overview & Buying OpenCV Computer Vision Application Programming Cookbook Second Edition
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OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

By : Robert Laganiere
3.7 (3)
Buy this Book
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OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

3.7 (3)
By: Robert Laganiere
Buy this Book

Overview of this book

OpenCV 3 Computer Vision Application Programming Cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.
Table of Contents (13 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. Playing with Images
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1. Playing with Images
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Introduction
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Installing the OpenCV library
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Loading, displaying, and saving images
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Exploring the cv::Mat data structure
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Defining regions of interest
2
2. Manipulating Pixels
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2. Manipulating Pixels
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Introduction
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Accessing pixel values
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Scanning an image with pointers
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Scanning an image with iterators
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Writing efficient image-scanning loops
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Scanning an image with neighbor access
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Performing simple image arithmetic
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Remapping an image
3
3. Processing Color Images with Classes
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3. Processing Color Images with Classes
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Introduction
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Using the Strategy pattern in an algorithm design
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Using a Controller design pattern to communicate with processing modules
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Converting color representations
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Representing colors with hue, saturation, and brightness
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4. Counting the Pixels with Histograms
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4. Counting the Pixels with Histograms
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Introduction
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Computing the image histogram
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Applying look-up tables to modify the image appearance
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Equalizing the image histogram
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Backprojecting a histogram to detect specific image content
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Using the mean shift algorithm to find an object
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Retrieving similar images using the histogram comparison
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Counting pixels with integral images
5
5. Transforming Images with Morphological Operations
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5. Transforming Images with Morphological Operations
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Introduction
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Eroding and dilating images using morphological filters
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Opening and closing images using morphological filters
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Detecting edges and corners using morphological filters
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Segmenting images using watersheds
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Extracting distinctive regions using MSER
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Extracting foreground objects with the GrabCut algorithm
6
6. Filtering the Images
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6. Filtering the Images
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Introduction
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Filtering images using low-pass filters
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Filtering images using a median filter
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Applying directional filters to detect edges
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Computing the Laplacian of an image
7
7. Extracting Lines, Contours, and Components
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7. Extracting Lines, Contours, and Components
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Introduction
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Detecting image contours with the Canny operator
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Detecting lines in images with the Hough transform
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Fitting a line to a set of points
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Extracting the components' contours
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Computing components' shape descriptors
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8. Detecting Interest Points
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8. Detecting Interest Points
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Introduction
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Detecting corners in an image
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Detecting features quickly
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Detecting scale-invariant features
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Detecting FAST features at multiple scales
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9. Describing and Matching Interest Points
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9. Describing and Matching Interest Points
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Introduction
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Matching local templates
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Describing local intensity patterns
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Describing keypoints with binary features
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10. Estimating Projective Relations in Images
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10. Estimating Projective Relations in Images
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Introduction
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Calibrating a camera
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Computing the fundamental matrix of an image pair
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Matching images using a random sample consensus
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Computing a homography between two images
11
11. Processing Video Sequences
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11. Processing Video Sequences
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Introduction
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Reading video sequences
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Processing the video frames
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Writing video sequences
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Tracking feature points in a video
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Extracting the foreground objects in a video
12
Index
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Index

Detecting image contours with the Canny operator

In the previous chapter, we learned how it is possible to detect the edges of an image. In particular, we showed you that by applying a threshold on the gradient magnitude, a binary map of the main edges of an image can be obtained. Edges carry important visual information since they delineate the image elements. For this reason, they can be used, for example, in object recognition. However, simple binary edge maps suffer from two main drawbacks. First, the edges that are detected are unnecessarily thick; this makes the object's limit more difficult to identify. Second, and more importantly, it is often impossible to find a threshold that is sufficiently low in order to detect all important edges of an image and is, at the same time, sufficiently high in order to not include too many insignificant edges. This is a trade-off problem that the Canny algorithm tries to solve.

How to do it...

The Canny algorithm is implemented in OpenCV by the ...

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Tech Concepts
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Programming languages
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OpenCV Computer Vision Application Programming Cookbook Second Edition
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