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Tag: image processing

OpenCV #010 Detecting Circles

Highlights: In this post we will learn about analysing a given image to find circles detected in that image. Tutorial Overview: Intro Detecting Circles with Hough Hough Transform for Circles Code 1. Intro In the previous post, we saw how we can detect and find lines on images using Hough Transform. Now let’s move to something just a little bit more complicated, circles. Let’s start with the equation of a circle: $$ ( x_{i}-a )^{2}+(…
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OpenCV #009 Detecting Lines

  Highlights: In this post, we will learn how to analyze images and detect basic features: lines! We will describe a well known Hough transform that will help us to do this task. Let’s roll! Tutorial Overview: Line Detection Hough Space Polar Representation for Lines Code for Detecting Lines in Python and C++ 1. Line Detection Human driver on a regular day performs lane detection. This is a crucial task in order to keep the…
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OpenCV #006 Sobel operator and Image gradient

Digital Image Processing using OpenCV (Python & C++) Highlights: In this post, we will learn what Sobel operator and an image gradient are. We will show how to calculate the horizontal and vertical edges as well as edges in general. What is the most important element in the image? Edges! See below. Tutorial Overview: What is the gradient? Finite difference. Discrete gradient. Sobel Operator. 1. What is the Gradient? Let’s talk about differential operators. When…
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OpenCV #005 Averaging and Gaussian filter

Digital Image Processing using OpenCV (Python & C++) Highlights: In this post, we will learn how to apply and use an Averaging and a Gaussian filter. We will also explain the main differences between these filters and how they affect the output image. What does make a good filter? This is a million dollar question. Tutorial Overview: Averaging filter Gaussian filter 1. Averaging filter What does make a good filter? So, let’s start with a…
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OpenCV #004 Common Types of Noise

Digital Image Processing using OpenCV (Python & C++) Highlights: We will give an overview of the most common types of noise that is present in images. We will show how we can generate these types of noise and add them to clean images. Then, we will show how we can filter these images using a simple median filter. In this post, we will assume that we “know” how the noise looks like in our experiments…
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