6. Filtering the Images | OpenCV Computer Vision Application Programming Cookbook Second Edition

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Table Of Contents

OpenCV Computer Vision Application Programming Cookbook Second Edition

By : Robert Laganiere

3.7 (3)

OpenCV Computer Vision Application Programming Cookbook Second Edition

3.7 (3)

By: Robert Laganiere

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Playing with Images

1. Playing with Images

Introduction

Installing the OpenCV library

Loading, displaying, and saving images

Exploring the cv::Mat data structure

Defining regions of interest

2. Manipulating Pixels

2. Manipulating Pixels

Introduction

Accessing pixel values

Scanning an image with pointers

Scanning an image with iterators

Writing efficient image-scanning loops

Scanning an image with neighbor access

Performing simple image arithmetic

Remapping an image

3. Processing Color Images with Classes

3. Processing Color Images with Classes

Introduction

Using the Strategy pattern in an algorithm design

Using a Controller design pattern to communicate with processing modules

Converting color representations

Representing colors with hue, saturation, and brightness

4. Counting the Pixels with Histograms

4. Counting the Pixels with Histograms

Introduction

Computing the image histogram

Applying look-up tables to modify the image appearance

Equalizing the image histogram

Backprojecting a histogram to detect specific image content

Using the mean shift algorithm to find an object

Retrieving similar images using the histogram comparison

Counting pixels with integral images

5. Transforming Images with Morphological Operations

5. Transforming Images with Morphological Operations

Introduction

Eroding and dilating images using morphological filters

Opening and closing images using morphological filters

Detecting edges and corners using morphological filters

Segmenting images using watersheds

Extracting distinctive regions using MSER

Extracting foreground objects with the GrabCut algorithm

6. Filtering the Images

6. Filtering the Images

Introduction

Filtering images using low-pass filters

Filtering images using a median filter

Applying directional filters to detect edges

Computing the Laplacian of an image

7. Extracting Lines, Contours, and Components

7. Extracting Lines, Contours, and Components

Introduction

Detecting image contours with the Canny operator

Detecting lines in images with the Hough transform

Fitting a line to a set of points

Extracting the components' contours

Computing components' shape descriptors

8. Detecting Interest Points

8. Detecting Interest Points

Introduction

Detecting corners in an image

Detecting features quickly

Detecting scale-invariant features

Detecting FAST features at multiple scales

9. Describing and Matching Interest Points

9. Describing and Matching Interest Points

Introduction

Matching local templates

Describing local intensity patterns

Describing keypoints with binary features

10. Estimating Projective Relations in Images

10. Estimating Projective Relations in Images

Introduction

Calibrating a camera

Computing the fundamental matrix of an image pair

Matching images using a random sample consensus

Computing a homography between two images

11. Processing Video Sequences

11. Processing Video Sequences

Introduction

Reading video sequences

Processing the video frames

Writing video sequences

Tracking feature points in a video

Extracting the foreground objects in a video

Index

Index

Filtering images using low-pass filters

In this first recipe, we will present some very basic low-pass filters. In the introductory section of this chapter, we learned that the objective of such filters is to reduce the amplitude of the image variations. One simple way to achieve this goal is to replace each pixel by the average value of the pixels around it. By doing this, the rapid intensity variations will be smoothed out and thus replaced by a more gradual transition.

How to do it...

The objective of the cv::blur function is to smooth an image by replacing each pixel with the average pixel value computed over a rectangular neighborhood. This low-pass filter is applied as follows:

   cv::blur(image,result,
            cv::Size(5,5)); // size of the filter

This kind of filter is also called a box filter. Here, we applied it by using a 5x5 filter in order to make the filter's effect more visible. Take a look at the following screenshot:

The result of the filter being applied on the preceding...

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Programming languages

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