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
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