cs6550 computer vision - nthu -...
TRANSCRIPT
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CS6550 Computer Vision
• Class Meeting:
M7M8 (3:30pm – 5:20pm), R6 (2:20pm – 3:10pm).
Rm 106 Delta Bldg.,
台達館 106室
• Instructor: Prof. Shang-Hong Lai, Rm. 636 Delta Bldg.,
賴尚宏,台達館636室,
Tel: ext. 42958, Email: [email protected],
URL: http://www.cs.nthu.edu.tw/~lai
• Office Hours: R7R8 or by appointment
• Teaching Assistant:
李東穎、蘇宏任, 台達館 720、721, CV lab.
Email: [email protected]; [email protected]
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Prerequisite
• Linear Algebra
• Probability and Statistics
• Basic Programming
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Course Description
• This course is to provide an introductory background
in computer vision for graduate students to start
research in this field.
• We will focus on teaching representative computer
vision algorithms in class.
• You will need to implement some algorithms with
computer programs for the homeworks and the final
project.
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Course Contents 1. Image Formation (1 week)
2. Image Features (2 weeks)
3. Image Segmentation (2 weeks)
4. Camera Calibration (1 week)
5. Two-View Geometry (1 week)
6. Stereo Reconstruction (1 week)
7. Image Matching (1 week)
8. Motion Analysis (1 week)
9. Object Recognition (1 week)
10. Augmented Reality (1 week)
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Textbooks • Primary:
Computer Vision: Algorithms and Applications,
by Richard Szeliski, draft (9/3/2010 version)
http://szeliski.org/Book/
•Secondary:
• Computer Vision: A Modern Approach, by
David Forsyth and Jean Ponce, Prentice Hall,
2003.
• Image Processing, Analysis, and Machine
Vision, by M. Sonka, V. Hlavac, R. Boyle,
Thomson Engineering, 3rd Edition, 2007(8).
• Lecture slides distributed in class.
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Sample Contents
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Grading
Midterm Exam. (11/26) 30%
Final Project 20%
Homeworks (4) 40%
Class Participation 5%
Quizzes 5%
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Homework Policy
• Discussion of homework is encouraged, but you
have to write your own. No copying is strictly
enforced.
• Homework should be delivered before the
announced due time, normally before the lecture.
Late homework will be degraded by 25% per day.
• No make-up homeworks 4 days after the deadline.
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Course Webpage
• http://cv.cs.nthu.edu.tw/courses.php
• It contains the course slides, basic course
information, and class announcement.
• Important course announcement will also be
posted on this webpage.
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Class Participation
• Class attendance is required and treated as the
basic requirement for class participation.
• Asking questions is strongly encouraged.
• Extra credit will be given for finding mistakes or
asking questions.
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CS 6550 Classroom Rule
• No eating is permitted in class.
• No sleeping is allowed in class.
• Disturbance to others in class should be
minimized.
• Cell phone should be turned off during the
class.
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Computer Vision
Make computers understand images and video.
What kind of scene?
Where are the cars?
How far is the
building?
…
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What is Computer Vision?
• To extract useful information about real physical
objects and scenes from sensed images/video.
– 3D reconstruction from images
– Object detection/recognition
• Automatic understanding of images and video
– Computing properties of the 3D world from visual data
(measurement)
– Algorithms and representations to allow a machine to
recognize objects, people, scenes, and activities.
(perception and interpretation)
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Vision for measurement
Real-time stereo Structure from motion
NASA Mars Rover
Pollefeys et al.
Multi-view stereo for
community photo collections
Goesele et al.
Slide credit: L. Lazebnik
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sky
wate
r
Ferris
wheel
amusement park
Cedar
Point
12 E
tree
tree
tree
carousel deck
people waiting in
line
rid
e
rid
e rid
e
umbrellas
pedestrians
maxair
bench
tree
Lake Erie
people sitting on
ride
Objects
Activities
Scenes
Locations
Text / writing
Faces
Gestures
Motions
Emotions…
The
Wicked
Twister
Vision for perception, interpretation
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Related Disciplines
Cognitive
science Algorithms
Image
processing
Graphics Machine
learning
Artificial
intelligence
Computer
vision
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Vision and Graphics
Model Images Vision
Graphics
Inverse problems: analysis and synthesis.
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Why computer vision?
• As image sources multiply, so do applications
– Relieve humans of boring, easy tasks
– Enhance human abilities: human-computer interaction,
visualization
– Perception for robotics / autonomous agents
– Organize and give access to visual content
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Why computer vision? • Images and videos are everywhere!
Personal photo albums
Surveillance and security
Movies, news, sports
Medical and scientific images
Slide credit; L. Lazebnik
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Why computer vision matters?
Safety Health Security
Comfort Access Fun
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Again, what is computer vision?
• Mathematics of geometry of image formation?
• Statistics of the natural world?
• Models for neuroscience?
• Engineering methods for matching images?
• Science Fiction?
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Very brief history of computer vision
• 1966: Minsky assigns computer vision as an undergrad summer project
• 1960’s: interpretation of synthetic worlds
• 1970’s: some progress on interpreting selected images
• 1980’s: ANNs come and go; shift toward geometry and increased mathematical rigor
• 1990’s: face recognition; statistical analysis in vogue
• 2000’s: broader recognition; large annotated datasets available; computational photography starts
Guzman ‘68
Ohta Kanade ‘78
Turk and Pentland ‘91
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Applications of Computer Vision
• Robot Vision / Autonomous Vehicles
• Biometric Identification / Recognition
• Industrial Inspection
• Video Surveillance
• Digital Camera
• Medical Image Analysis/Processing
• Remote Sensing
• Multimedia Retrieval
• Augmented Reality
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Consumer Applications
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(a) image stitching: merging different views (Szeliski and Shum
1997) (b) exposure bracketing: merging different exposures.
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Real-Time Stereo Camera
Bumblebee
Point Grey Research makes video
rate stereo camera (640 x 480 at
30 fps).
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3D Reconstruction from Images
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Earth viewers (3D modeling)
Image from Microsoft’s Virtual Earth
(see also: Google Earth)
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Photosynth
http://photosynth.net/
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Object Detection
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Optical Character Recognition (OCR)
Digit recognition, AT&T labs
http://www.research.att.com/~yann/
Technology to convert scanned docs to text • If you have a scanner, it probably came with OCR software
License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
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Face Detection
• Many new digital cameras now detect faces
– Canon, Sony, Fuji, …
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Smile detection?
Sony Cyber-shot® T70 Digital Still Camera 32
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Face Detection and Recognition
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Face detection algorithms, coupled with color-based clothing and hair detection
algorithms, can locate and recognize the individuals in this image (Sivic,
Zitnick, and Szeliski 2006)
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Biometric Recognition
Who is she? 34
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Vision-based Biometrics
How the Afghan Girl was Identified by Her Iris Patterns?
http://www.cl.cam.ac.uk/~jgd1000/afghan.html
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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/
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Object Recognition (in mobile phones)
• This is becoming real:
– Google goggles
– Point & Find, Nokia
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The Matrix movies, ESC Entertainment, XYZRGB, NRC
Special effects: shape capture
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Sports – Augmented Reality
Sportvision first down line
Nice explanation on www.howstuffworks.com 39
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Google street view
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Google street view
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Smart Cars
• Mobileye
– Vision systems currently in high-end BMW, GM, Volvo models
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Assisted Driving
meters
met
ers
Ped
Ped
Car
Lane detection
Pedestrian and car detection
• Collision warning
systems with adaptive
cruise control,
• Lane departure
warning systems,
• Rear object detection
systems,
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Google Autonomous Car
• The U.S. state of Nevada passed a law in June 2011
concerning the operation of driverless cars in Nevada.
• The Google Driverless Car combines information
gathered from Google Street View, video cameras inside
the car, a LIDAR sensor on top of the vehicle, radar
sensors on the front of the vehicle and a position sensor
attached to one of the rear wheels.
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Vision-based Interaction
Nintendo Wii has camera-based IR
tracking built in.
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Control games with your own body
motion/gesture and create immersive
experiences by combining 3D personal
image into the game scene
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Vision in Space
Vision systems (JPL) used for several tasks • Panorama stitching
• 3D terrain modeling
• Obstacle detection, position tracking
NASA'S Mars Exploration Rover Spirit captured this westward view from atop
a low plateau where Spirit spent the closing months of 2007.
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Robotics
NASA’s Mars Spirit Rover
http://en.wikipedia.org/wiki/Spirit_rover
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Darpa’s Robotics Challenge
http://www.darpa.mil/Our_Work/TTO/Progra
ms/DARPA_Robotics_Challenge.aspx
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Medical Imaging
Image guided surgery
Grimson et al., MIT 3D imaging
MRI, CT
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Augmented Reality
• AR allows the user to see the real world, with virtual objects
superimposed upon or composited with the real world.
Therefore, AR supplements reality, rather than completely
replacing it.
• Google Glasses is a research and development program to
develop an augmented reality head-mounted display (HMD).
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Virtual Dressing Room
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• Read Chap. 1 (Szeliski)
• Next classes
– Introduction to Matlab programming
– Image formation (Chap. 2, Szeliski)
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Things to Do