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# Tag: Computer Vision

### CamCal 003 Camera Transformation

Highlights: In this post we will talk about camera calibration technique, and rigid body transformations. You will also see how translation and rotation works. Tutorial Overview: Intro Geometric camera calibration Rigid body transformations﻿ Notation Rotation 1. Intro In this post, we’re going to talk about extrinsic camera calibration and here is the model that we are going to use. In particular, we had a system where we had a center of projection (COP) that was…

### CamCal #012 Stereo Geometry Code

Highlights: In this post we will finish our mini series on stereo geometry. We will wrap some things up, and go through the code related to this. In the last few posts we talked about stereo geometry. We covered many concepts, from basic stuff, such as essential matrix and epipolar lines to the fundamental matrix. Here, we will present a code that can help us to compute them. Tutorial Overview: Detecting key points (SIFT, SURF,…

### CamCal 010 Essential Matrix Computation – an example

Highlights: In this post we will show some essential matrix computation for the example of parallel cameras. In addition, you will see why it can be useful for. Tutorial Overview: This post covers the following topics: Computation The use of the Essential Matrix 1. Computation In the last post we talked about essential matrix and what it does. Now we are going to show how it is computed. So we have two parallel image planes,…
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}+(…