Camera Calibration and Stereo Vision - Master Data Science

#000 CamCal Perspective Imaging

Perspective Imaging

How we can better understand and model the process of mapping our 3D world into a 2D image

#001 CamCal Perspective Projection

Parallel lines in the world and parallel lines in the image

How parallel lines behave differently both in real world and in perspective projection?

#002 CamCal Weak Perspective

Weak perspective model

Differences between human vision and perspective projection

#003 CamCal Camera Transformation

Rigid body transformations

Geometric relationship between the camera and the world

#004 CamCal What does R look like?

Rotation about z-axes

A detailed look how does rotation operator look like

#005 CamCal Translation and Rotation

The rotation matrix and the translation vector

Process of going from World to Camera frame. In order to that we need to apply translation and rotation

#006 CamCal Ideal vs Real Intrinsic Parameters

The main difference between ideal and real intrinsic parameters

#007 CamCal Camera Calibration

Calibration pattern.

The main idea behind Camera Calibration

#008 CamCal Stereo Geometry Intro

Projective lines defined

Learn about point and line duality and how to find the line given two points

#009 CamCal Stereo Geometry – Epipolar lines and Essential Matrix

Stereo correspondence

The relationship between two calibrated cameras

#010 CamCal Essential Matrix Computation – an example

Example for parallel cameras

Why is the use of the essential matrix important in computer vision?

#011 CamCal Fundamental Matrix

Fundamental Matrix properties

What is the relationship between the pixel coordinates in the two views?

#012 CamCal Stereo Geometry Code

Fundamental Matrix properties

In this post we will present a code that can help us to compute essential matrix, epipolar lines and fundamental matrix