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CS4670/5670: Intro to Computer Vision Instructor: Kavita Bala
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Instructor
• Kavita Bala ([email protected])
• Office hours:
F: 10-‐11, or by appointment
• Research interests: – Computer graphics and vision
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Today
1. What is computer vision?
2. Course overview
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Today
• Readings – Szeliski, Chapter 1 (IntroducTon)
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Every image tells a story
• Goal of computer vision: perceive the “story” behind the picture
• Compute properTes 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 percepTon?
• Yes and no (mainly no) – computers can be be]er at “easy” things
– humans are much be]er at “hard” things
• But huge progress has been made – Especially in the last 10 years
– What is considered “hard” keeps changing
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Human percepTon has its shortcomings
Sinha and Poggio, Nature, 1996
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But humans can tell a lot about a scene from a li]le informaTon…
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 • CompuTng the 3D shape of the world
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The goal of computer vision • Recognizing objects and people
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slide credit: Fei-Fei, Fergus & Torralba
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sky
building
flag
wall banner
bus
cars
bus
face
street lamp
slide credit: Fei-Fei, Fergus & Torralba
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The goal of computer vision • “Enhancing” images (c.f. ComputaTonal Photography)
Texture synthesis / increased field of view (uncropping) (image credit: Efros and Leung)
InpainTng / image compleTon (image credit: Hays and Efros)
Super-‐resoluTon / denoising (source: 2d3)
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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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Why study computer vision?
• Millions of images being captured all the Tme
• Loads of useful applicaTons • The next slides show the current state of the art
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OpTcal character recogniTon (OCR)
Digit recogniTon, AT&T labs h]p://www.research.a].com/~yann/
• If you have a scanner, it probably came with OCR soiware
License plate readers h]p://en.wikipedia.org/wiki/AutomaTc_number_plate_recogniTon
Source: S. Seitz AutomaTc check processing
Sudoku grabber h]p://sudokugrab.blogspot.com/
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Face detecTon
• Many new digital cameras now detect faces – Canon, Sony, Fuji, …
Source: S. Seitz
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Face RecogniTon
http://developers.face.com/tools/
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Face recogniTon
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 laptops,
other devices
Face recognition systems now beginning to appear more widely
http://www.sensiblevision.com/
Source: S. Seitz
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Object recogniTon (in supermarkets)
LaneHawk by EvolutionRobotics “A smart camera is flush-mounted in the checkout lane, continuously watching for items. When an item is detected and recognized, the cashier verifies the quantity of items that were found under the basket, and continues to close the transaction. The item can remain under the basket, and with LaneHawk,you are assured to get paid for it… “
Source: S. Seitz
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Google Goggles
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Google Search by Image
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Leaf RecogniTon
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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: moTon capture
Source: S. Seitz
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Sports
Sportvision first down line Nice explanaTon on www.howstuffworks.com
Source: S. Seitz
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Vision-‐based interac/on (and games)
Kinect
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Smart cars
• Mobileye – Vision systems currently in high-‐end cars
Sources: A. Shashua, S. Seitz
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Smart cars
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Vision in space
Vision systems (JPL) uses for several tasks • Panorama stitching • 3D terrain modeling • Obstacle detection, position tracking • For more, read “Computer Vision on Mars” by Matthies et al.
The Heights of Mount Sharp http://www.nasa.gov/mission_pages/msl/multimedia/pia16077.html Panorama captured by Curiosity Rover, August 18, 2012 (Sol 12)
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RoboTcs
NASA’s Mars Curiosity Rover (Mars Science Laboratory) h]p://en.wikipedia.org/wiki/Spirit_rover
Autonomous RC Car h]p://www.cs.cornell.edu/~asaxena/rccar/
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Medical imaging
Image guided surgery Grimson et al., MIT
3D imaging MRI, CT
Source: S. Seitz
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Large-‐scale 3D reconstrucTon • AutomaTc 3D reconstrucTon from Internet photo collecTons
“Statue of Liberty”
3D model
Flickr photos
“Half Dome, Yosemite” “Colosseum, Rome”
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Photosynth
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Current state of the art • You just saw examples of current systems.
– Many of these are less than 5 years old
• This is a very acTve research area, and rapidly changing – Many new apps in the next 5 years
• To learn more about vision applicaTons and companies – David Lowe maintains an excellent overview of vision companies
• h]p://www.cs.ubc.ca/spider/lowe/vision.html
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Why is computer vision difficult?
Viewpoint variaTon
IlluminaTon Scale
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Why is computer vision difficult?
Intra-‐class variaTon
Background clu]er
MoTon (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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Bo]om line • PercepTon is an inherently ambiguous problem
– Many different 3D scenes could have given rise to a parTcular 2D picture
– We oien need to use prior knowledge about the structure of the world
Image source: F. Durand
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Instructor
• Kavita Bala ([email protected])
• Office hours:
F: 10-‐11, or by appointment
• Research interests: – Computer graphics and vision – Rendering, PercepTon, Material modeling and recogniTon
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Autodesk 360 Cloud Render
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Current Interests
• Material PercepTon • RecogniTon
– Visual Search • Scene ReconstrucTon
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Important personnel • TAs:
– Senior TAs • Sco] Wehrwein, Sean Bell
– PhD TAs • Balazs Kovacs, Andreas Veit
– Meng TA • Alec Regulinski
– Undergrad TAs • Akhila Ananthram, Daniel Carpenter, Sheroze Sheriffdeen, Dhruv Singhal, Raghav Subramaniam
• Office hours TBA
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Other administraTve details
• Textbook: Rick Szeliski, Computer Vision: Algorithms and Applica;ons
online at: h]p://szeliski.org/Book/
• Course webpage (lectures, assignments): h]p://www.cs.cornell.edu/courses/cs4670/2015sp/
• Announcements/grades via Piazza/CMS Sign up on piazza h]ps://cms.csuglab.cornell.edu/
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Course requirements
• Prerequisites—these are essen;al! – Data structures – A good working knowledge of C/C++ programming – Linear algebra – Vector calculus
• Course does not assume prior imaging experience – computer vision, image processing, graphics, etc.
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Course overview (tentaTve) 1. Low-‐level vision
– image processing, edge detecTon, feature detecTon, cameras, image formaTon
2. Geometry and algorithms – projecTve geometry, stereo,
structure from moTon, Markov random fields
3. RecogniTon – detecTon / recogniTon, category
recogniTon, segmentaTon
4. Light, color, and reflectance
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1. Low-‐level vision
• Basic image processing and image formaTon
Filtering, edge detecTon
* =
Feature extracTon Image formaTon
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Project: Image Scissors
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Project: Feature detecTon and matching
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2. Geometry
ProjecTve geometry
Stereo
MulT-‐view stereo Structure from moTon
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Project: CreaTng panoramas
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3. RecogniTon
Sources: D. Lowe, L. Fei-Fei
Face detecTon and recogniTon Single instance recogniTon
Category recogniTon
Single instance recogniTon
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4. Light, color, and reflectance
Light & Color Reflectance
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Grading
• Occasional quizzes (at the beginning of class) • One prelim, one final exam
• Rough grade breakdown: – Quizzes: 2-‐5% – Midterm: 15-‐20%
– Homeworks: 10-‐20% – Programming projects: 40-‐50% – Final exam: 15-‐20%
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Late policy
• Three “late days” will be available for the semester
• Late projects will be penalized by 25% for each day it is late, and no extra credit will be awarded.
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Academic Integrity
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QuesTons?