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This lecture introduces the 2D camera calibration process, and the role of a checker-board pattern, which is typically used in calibration cameras. Camera calibration requires computing both the Camera Matrix, K, as well as the lens distortion parameters to counter the aperture distortion induced in the images. Both of these processes are covered in this course.

AI-1.0X: Machine Learning Regularization: Iterative Soft Thresholding Algorithm

AI-1.0X: Machine Learning Regularization: Soft Thresholding with L1 Norm Regularization

AI-1.0X: Machine Learning Regularization: Understanding Majorization Function

AI-1.0X: Machine Learning Regularization: Minimization Landweber Iteration

AI-1.0X: Machine Learning Regularization: Majorization Function Selection for Least Squares

AI-1.0X: Machine Learning Regularization: Majorization Minimization Algorithm

AI-1.0X: Machine Learning Regularization: Majorization Minimization Principle

AI-1.0X: Machine Learning Regularization: ISTA and Introduction to Majorization Minimization

AI-1.0X: Machine Learning Regularization: Subgradient Descent for L1 Regularized Linear Regression

AI-1.0X: Machine Learning Regularization: Subgradient Descent and ISTA Introduction

AI-1.0X: Machine Learning Regularization: Sub-gradients

AI-1.0X: Machine Learning Regularization: Operations that preserve the convexity of a function

AI-1.0X: ML Regularization: Second order condition of Convexity and Link to Machine Learning

AI-1.0X: Machine Learning Regularization: Proof of Sufficiency for 1st Order Condition for N D case

AI-1.0X: ML Regularization: Proof of First Order Condition of Convexity - Necessary N-D case

AI-1.0X: Machine Learning Regularization: Proof of First Order Condition of Convexity Sufficiency

AI-1.0X: ML Regularization: Proof of First Order Condition of Convexity Necessary Condition

AI 1 0X: Machine Learning Regularization: First Order Condition of Convexity of Function

AI 1 0X: Machine Learning Regularization: First Order Condition of Convexity of Function

AI-1.0X: Machine Learning Regularization: Extended Value Extension of Convex Functions

AI-1.0X: Machine Learning Regularization: Convex Function Property

AI-1.0X: Machine Learning Regularization: Convex Functions and Convex Sets

AI-1.0X: Machine Learning Regularization: Convex Optimization Introduction

AI-1.0X: Machine Learning Regularization: L2 Norm Regularization

AI-1.0X: Machine Learning Regularization: L1 and L2 Norm

AI-1.0X: Machine Learning Regularization: MAP Estimate and Regularization

AI-1.0X: Machine Learning Regularization: Maximum Aposteriori Probability Estimate

RO-1.0X037: Histogram Equalization Algorithm