Connected components and graph traversal Prof. Noah Snavely CS1114 .
Instructor: Noah Snavely - Cornell University · “easy” things –humans are much better at...
Transcript of Instructor: Noah Snavely - Cornell University · “easy” things –humans are much better at...
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CS5670: Intro to Computer VisionInstructor: Noah Snavely
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Instructor
• Noah Snavely ([email protected])
• Research interests:
– Computer vision and graphics
– 3D reconstruction and visualization of Internet photo collections
– Deep learning for computer graphics
– Virtual and augmented reality
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Teaching Assistants
• Kai Zhang ([email protected])
• Qianqian Wang ([email protected])
• Please check course webpage for office hours
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Today
1. What is computer vision?
2. Course overview
3. Image filtering
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Today
• Readings
– Szeliski, Chapter 1 (Introduction)
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Every image tells a story
• Goal of computer vision: perceive the “story” behind the picture
• Compute properties of the world
– 3D shape
– Names of people or objects
– What happened?
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The goal of computer vision
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Can the computer match human perception?
• Yes and no (mainly no)– computers can be better at
“easy” things– humans are much better at
“hard” things
• But huge progress has been made– Accelerating in the last 4
years due to deep learning– What is considered “hard”
keeps changing
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Human perception has its shortcomings
Sinha and Poggio, Nature, 1996
(“The Presidential Illusion”
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But humans can tell a lot about a scene from a little information…
Source: “80 million tiny images” by Torralba, et al.
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The goal of computer vision
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The goal of computer vision• Compute the 3D shape of the world
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The goal of computer vision
• Recognize objects and people
Terminator 2, 1991
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slide credit: Fei-Fei, Fergus & Torralba
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sky
building
flag
wallbanner
bus
cars
bus
face
street lamp
slide credit: Fei-Fei, Fergus & Torralba
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The goal of computer vision• “Enhance” images
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The goal of computer vision
• Forensics
Source: Nayar and Nishino, “Eyes for Relighting”
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Source: Nayar and Nishino, “Eyes for Relighting”
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Source: Nayar and Nishino, “Eyes for Relighting”
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The goal of computer vision• Improve photos (“Computational Photography”)
Inpainting / image completion (image credit: Hays and Efros)
Super-resolution (source: 2d3)Low-light photography
(credit: Hasinoff et al., SIGGRAPH ASIA 2016)
Depth of field on cell phone camera (source: Google Research Blog)
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Why study computer vision?
• Billions of images/videos captured per day
• Huge number of useful applications
• The next slides show the current state of the art
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Optical character recognition (OCR)
Digit recognition, AT&T labs (1990’s)http://yann.lecun.com/exdb/lenet/
• If you have a scanner, it probably came with OCR software
License plate readershttp://en.wikipedia.org/wiki/Automatic_number_plate_recognition
Automatic check processing
Sudoku grabberhttp://sudokugrab.blogspot.com/
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Face detection
• Nearly all cameras detect faces in real time
– (Why?)
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Face Recognition
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Face recognition
Who is she? Source: S. Seitz
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Vision-based biometrics
“How the Afghan Girl was Identified by Her Iris Patterns” Read the story
Source: S. Seitz
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Login without a password
Fingerprint scanners on
many new smartphones
and other devices
Face unlock on Apple iPhone X
See also http://www.sensiblevision.com/
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Bird Identification
Merlin Bird ID (based on Cornell Tech technology!)
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Special effects: camera tracking
Boujou, 2d3
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The Matrix movies, ESC Entertainment, XYZRGB, NRC
Special effects: shape capture
Source: S. Seitz
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Pirates of the Carribean, Industrial Light and Magic
Special effects: motion capture
Source: S. Seitz
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3D face tracking w/ consumer cameras
Snapchat Lenses
Face2Face system (Thies et al.)
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Image synthesis
Karras, et al., Progressive Growing of GANs for Improved Quality, Stability, and Variation, ICLR 2018
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Image synthesis
Zhu, et al., Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, ICCV 2017
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Sports
Sportvision first down lineNice explanation on www.howstuffworks.com
Source: S. Seitz
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Smart cars
• Mobileye
• Tesla Autopilot
• Safety features in many high-end cars
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Self-driving cars
Google Waymo
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Robotics
NASA’s Mars Curiosity Roverhttps://en.wikipedia.org/wiki/Curiosity_(rover)
Amazon Picking Challengehttp://www.robocup2016.org/en/events
/amazon-picking-challenge/
Amazon Prime Air
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Medical imaging
3D imaging (MRI, CT)
Skin cancer classification with deep learning https://cs.stanford.edu/people/esteva/nature/
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Virtual & Augmented Reality
6DoF head tracking Hand & body tracking
3D-360 video capture3D scene understanding
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My own work
• Automatic 3D reconstruction from Internet photo collections
“Statue of Liberty”
3D model
Flickr photos
“Half Dome, Yosemite” “Colosseum, Rome”
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Photosynth
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City-scale reconstruction
Reconstruction of Dubrovnik, Croatia, from ~40,000 images
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Depth from a single image
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Current state of the art
• You just saw many examples of current systems.
– Many of these are less than 5 years old
• This is a very active research area, and rapidly changing
– Many new apps in the next 5 years
– Deep learning powering many modern applications
• Many startups across a dizzying array of areas
– Deep learning, robotics, autonomous vehicles, medical imaging, construction, inspection, VR/AR, …
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Why is computer vision difficult?
Viewpoint variation
IlluminationScale
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Why is computer vision difficult?
Intra-class variation
Background clutter
Motion (Source: S. Lazebnik)
Occlusion
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Challenges: local ambiguity
slide credit: Fei-Fei, Fergus & Torralba
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But there are lots of cues we can exploit…
Source: S. Lazebnik
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Bottom line• Perception is an inherently ambiguous problem
– Many different 3D scenes could have given rise to a particular 2D picture
– We often need to use prior knowledge about the structure of the world
Image source: F. Durand
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CS5670: Introduction to Computer Vision
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Important notes
• Textbook:
Rick Szeliski, Computer Vision: Algorithms and Applications
online at: http://szeliski.org/Book/
• Course webpage: http://www.cs.cornell.edu/courses/cs5670/2018sp/
• Announcements/grades via Piazza/CMShttps://piazza.com/cornell/spring2018/cs5670
https://cmsx.cs.cornell.edu
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Course requirements
• Prerequisites—these are essential!
– Data structures
– A good working knowledge of Python programming
– Linear algebra
– Vector calculus
• Course does not assume prior imaging experience
– computer vision, image processing, graphics, etc.
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Course overview (tentative)
1. Low-level vision– image processing, edge 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
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1. Low-level vision
• Basic image processing and image formation
Filtering, edge detection
* =
Feature extraction Image formation
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Project: Hybrid images from image pyramids
G 1/4
G 1/8
Gaussian 1/2
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Project: Feature detection and matching
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2. Geometry
Projective geometry
Stereo
Multi-view stereo Structure from motion
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Project: Creating panoramas
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Project: Photometric Stereo
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3. Recognition
Sources: D. Lowe, L. Fei-Fei
Face detection and recognitionSingle instance recognition
Category recognition
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Project: Convolutional Neural Networks
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Grading
• Occasional quizzes (at the beginning of class)
• One prelim, one final exam
• Grade breakdown (subject to minor tweaks):
– Quizzes: 5%
– Midterm: 15-18%
– Programming projects: 60-65%
– Final exam: 15-18%
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Late policy
• Four free “slip days” will be available for the semester
• A late project will be penalized by 10% for each day it is late (excepting slip days), and no extra credit will be awarded.
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Academic Integrity
• Assignments will be done solo or in pairs (we’ll let you know for each project)
• Please do not leave any code public on GitHub (or the like) at the end of the semester!
• We will follow the Cornell Code of Academic Integrity (http://cuinfo.cornell.edu/aic.cfm)
• We reserve the right to run MOSS (automated code copying service) on submitted code
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Questions?