This repository contains my Lab and Practical work for my course on Visual Perception in Master of computer vision at Université de Bourgogne. The course was taught by Prof David Fofi and Devesh Adlakha.

Instruction on the Lab can be found here and Lecture can be found here. Please Take permission from the corresponding authors to use any lab instructions or lectures.

Lab Report can be found here Report-1 (Lab-1,Lab-3) and Report-2 (Lab-2,Lab-4).

Practical Exercise Report can be found here Report-1and Report-2.

Lab-1 Camera Calibrarion with DLT. matlabcode

Objective : The goal of this lab exercise is to understand and implement the DLT algorithm for camera calibration.

world for Camera Calibration.

QR Decomposition and Cholesky factorization.

Lab-2 Zhang Camera Calibration. matlabcode

Objective : The goal of this lab exercise is to understand and implement Zhang’s plane-based calibration method. You will test your implementation using simulated data. In the process, you will become more comfortable with developing and solving a linear system of equations, a process that is regularly used to solve computer vision problems.

Comparing Atucal Image and Randomely generated Noise image.

Comparing Atucal Image and Randomely generated Noise image.

Lab-3 Bouguet Toolbox for Matlab. matlabcode

Objective : The goal of this lab exercise is to become familiar with the camera calibration process in practice by using Bouguet’s toolbox. The practical experience in calibrating your camera will solidify your understanding of Zhang’s method as well as of the different processing steps involved (corner detection, estimating distortion parameters, optimization of the parameters, etc.).

Bouguet Toolbox for Camera Calibration.

Bouguet Toolbox for Camera Calibration.

Bouguet Toolbox for Camera Calibration.

Lab-4 Fundamental Matrix. matlabcode

Objective : The goal of this lab exercise is to understand and implement the normalized 8-point algorithm to linearly estimate the Fundamental matrix relating two images. Simulated data is provided for you to test your implementation and conduct further experiments.

Epipolar lines with Fundamental Matrix.

Practical Exercise-1 Triangulation. matlabcode

Triangulation with Noise.

Practical Exercise-2 Harris Corner Detector. matlabcode

CamraMan Image and corners in the image.

Derivative of in x and y of CamraMan Image.

Checker Pattern and corners detected on it.

Derivative of the checker board in x and y.