Image filtering: features: histogram of gradients (HOG)

Deepfakes

Ballistic Motion

3D Modelling

Specularities

Lighting

Human Abilities and Limits

Lens Flare

Shadows

Vanishing Points and Lines

Reflections

Introduction

Image understanding: unsupervised learning: tSNE: implementation

Closing: parting thoughts

Image understanding: unsupervised learning: t-distributed stochastic neighbor embedding (tSNE)

Image understanding: unsupervised learning: principal component analysis (PCA): implementation

Image understanding: unsupervised learning: principal component analysis (PCA): eigenfaces

Image understanding: unsupervised learning: principal component analysis (PCA): computation

Image understanding: unsupervised learning: principal component analysis (PCA): eigenvectors

Image understanding: unsupervised learning: principal component analysis (PCA): covariance matrix

Image understanding: unsupervised learning: principal component analysis (PCA): canonical basis

Image understanding: unsupervised learning: expectation/maximization: EM implementation

Image understanding: unsupervised learning: expectation/maximization: M-step

Image understanding: unsupervised learning: expectation/maximization: E-step

Image understanding: unsupervised learning: expectation/maximization: EM