01. Introduction

Organization

• What you can learn from this course?
• Syllabus
• Logistics: homework, project, attendance, grading

Introduction

• What is computer vision
• Computer vision vs. biological vision
• Computer vision vs. image processing & graphics
• Why study computer vision
• Why is visual perception hard (challenges)
• Applications

History

• Waves of development
• 60s: Block worlds, edges and model fitting
• 70s: Low-level vision: stereo, flow, shape-from-shading
• 80s: Neural networks, backpropagation, self-driving
• 90s: Dense stereo, multi-view geometry, MRFs
• 00: Features, descriptors, large-scale structure from motion
• 10-now: Deep learning, large datasets, commercialization
• Current challenges