[CS5670] Lecture 0: Intro to Computer Vision

 

1. What is computer vision?
2. Why study computer vision?
3. Course overview
4. Images & Image filtering [time permitting]

 

1. What is computer vision?

 

Goal of computer vision:

percieve the story behind the picture

 

Compute properties of the world

• 3D shape
• Names of people or objects
• What happened?

 

Can computers match human perception?

• Yes and No (maninly no)
  • computers can be better at 'easy' things
  • humans are better at 'hard' things
• But huge progress
  • Accelerating in the last five years due to deep learning
  • What is considered 'hard' keeps chaning

 

Human perception has its shortcomings

But humans can tell a lot about from a little information...

 

The goal of computer vision

 

• Compute the 3D shape of the world
• Recognize objects and people
• 'Enhance' images
• Forensics
• Improve photos ('Computational Photography')
  • Super-resolution
  • Low-light photography
  • Depth of field on cell phone camera
  • Inpainting / image completion

 

2. Why study computer vision?

 

• Bilions of images/videos captured per day
• Huge number of potential applications
• The next slide show the current state of the art(SOTA)

 

• Optical character recognition (OCR)
• Face detection (Nearly all cameras detect faces in real time (Why?)
• Face analysis and recognition
• Vision-based biometrics
• Login without a password
  • Fingerprint scanners on many new smartphones and other devices
  • Face unlock on Apple iPhone X
• Bird identification
• Special effects: shape capture
• Special effects: motion capture
• 3D face tracking w/ consumer cameras
• Image synthesis
  • Which face is real?
• Sports
• Smart cars
• Self-driving cars
• Robotics
• Medical imaging
• Virtual & Auamented Reality

 

Current state of the art

• You just saw many examples of current systems.
  • Many of these are loss than 5 years old
• Computer vision is an active research area, and rapidly changing
  • Many new apps in the next 5 years
  • Deep learning powering many modern applications
• Many startups acrosss a dizzying array of areas
  • Deep learning, robotics, autonomous vehicles, medical imaging, construction, inspection, VR/AR, ...

 

Why is computer vision difficult?

• Viewpoint variation
• Illumination
• Scale
• Intra-class variation
• Background clutter
• Motion
• Occlusion

 

Challenges: local ambiguity

But there are lots of visual cues we can use...

 

Bottom line

• Perception is an inherently ambiguous problem
  • Many different 3D scenes could have given rise to a given 2D image
  • We often must use prior knowledge about the world's structure

 

Project based course whose goal is to teach you the basics of computer vision image processing, geometry,
recognition in a hands on way.

 

Course requirements

• Prerequisites
  • Data structures
  • Good working knowledge of Python programming
  • Linear algebra
  • Vector calculus
• Course does not assume prior imaging experience
  • computer vision, image processing, graphics, etc.

 

3. Course overview (tentative)

1. Low-level vision
   • Image processing, edg detection, feature detection, cameras, image formation
2. Geometry and algorithms
   • projective geometry, stereo, structure from motion, optimization
3. Recognition
   • face detection/recognition, category recognition, segmentation

 

4. Images & Image filtering [time permitting]

 

1. Low-level vision

Basic image processing and image formation

• Filtering, edge detection
• Feature extraction
• Image formation

2. Geometry

• Projective geometry
• Stereo vision
• Multi-view stereo
• Structure from motion

 

3. Recognition

• Image classification
• Object detection
• Convolutional Neural Networks

 

 

 

 

Reference: CS5670: Intro to Computer Vision (Cornell Tech)