Download Report

Administration
• CSED441: Introduction to Computer Vision
CSED441:Introduction to Computer Vision (2015S)
Lecture1: Introduction to Computer Vision




Instructor: Prof. Bohyung Han ([email protected], B4‐123)
TA: TBD
Time & Location: TuTh 15:30 ~ 16:45 AM, B2‐105
Office hour
• Bohyung Han: Tue 17:00~18:00 or by appointment
• TA: TBD
 Textbook (for reference)
Bohyung Han
CSE, POSTECH
[email protected]
• Computer Vision: Algorithms and Applications by R. Szeliski (Sep 2010 Ed.)
• Computer Vision: A Modern Approach by D. Forsyth and J. Ponce
• Multiple View Geometry by R. Hartly and A. Zisserman
 Prerequisite: probability theory & linear algebra
 Course webpage: http://cv.postech.ac.kr/~bhhan/class/cse441_2015s.
html
2
(Tentative) Schedule
•
•
•
•
•
•
•
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Grading
Introduction and preliminaries (1.5 weeks)
Visual features (2.5 weeks)
Object detection and recognition (3.5 weeks)
Image segmentation and clustering (2 weeks)
Motion and tracking (2 weeks)
Deep Learning (1.5 weeks)
Image formation and geometric computer vision (2 weeks)
• 5 assignments (30%)
 Problem solving
 Small programming project
• Midterm exam (30%)
• Final exam (40%)
 Comprehensive
• Final project
 Programming/research project
 Final report
 Graduate student only
• Note:
 Thresholds for letter grades may be higher for graduate students.
 Individual percentages are subject to change.
3
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
4
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Final Project
Course Policy
• Team organization: individual project
• Deliverables
• Assignments submission
 Late assignments will not be accepted.
 But, you have one wildcard for 3‐day late submission. Use it smartly.
 Demo, source code, and presentation
 Intermediate reports
 Final report
• Academic integrity
• Guideline
 You decide the theme of your project.
 Final report should adhere to the standard quality and format of reputable conferences and journals.
 Top venues in computer vision: CVPR, ICCV, ECCV, TPAMI, IJCV
 If you do the fantastic job in your project in terms of both performance and presentation, you are eligible for ‘A’ regardless any other scores.
5
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
 Make sure to acknowledge the POSTECH academic integrity. Violating t
he academic integrity means the automatic failure (F) in this class with NO exception.
• All written communication should be in English.
 Homeworks, reports, exams, etc.
6
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Important Dates (Tentative)
• Assignment Dues  3/17(Tue), 4/2 (Thu), 4/16 (Thu), 5/12 (Tue), 6/2 (Tue)
 All assignments should be handed in BEFORE class.
• Midterm exam
 Date: 4/23 (Thu)
What is Computer Vision?
• Final exam
 Comprehensive
 Date: 6/18 (Thu)
• Final project
 Intermediate reports due: 4/9 (Thu), 5/7 (Thu), 5/21 (Thu)
 Presentation: TBA
 Final Report due: 6/21 (Sun)
7
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Understanding Images without Human Supervision
amusement park
laser
Sensing
camera
The Wicked Twister sky
Ferris wheel
water
12E
tree
People sitting on the ride
tree
building
deck
camera
merry‐go‐round
bench
radar
radar
umbrellas
human
People walking
9
10
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Characteristics of Computer Vision
Origin of Computer Vision
Computer
Vision
Machine
Learning
Image
Processing
11
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Psychology
Computer
Graphics
Statistics
Artificial Intelligence
• Convergence of various research disciplines
Machine Vision
• Automation
• Machine inspection
• Document processing
Robot Vision
• Visual SLAM
Computer Vision
• Visual recognition
Human Vision
• Cognitive science
• Neural computation
12
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Face Detection
Interesting Computer Vision Problems
Microsoft Kinect
Automotive Vision Systems
Autonomous Driving
Object Detection
Deformable part‐based modeling
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part‐Based Models. TPAMI, 2009
Image Classification and Deep Learning
Motion and Tracking
• Challenges
 Non‐rigid shape
 Appearance changes
 Occlusions
A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS 2012
C. Bibby, I.D. Reid. Robust Real‐Time Visual Tracking Using Pixel‐Wise Posterior. ECCV, 2008.
M. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014 19
Cropped target
Appearance
window
model
B. Han, L.S. Davis. Density‐Based On‐Line Appearance Modeling for Object Tracking. ICCV, 2005.
Human Activity Recognition
Event Detection
S. Kwak, B. Han, J.H. Han, Scenario‐
Based Video Event Recognition by Constraint Flow. CVPR, 2011.
V. Delaitre, I. Laptev, J. Sivic , Recognizing human actions in still images: a study of bag‐of‐features and part‐based representations, BMVC 2010
I. Laptive, M. Marszalek, C. Schmid , B. Rozenfeld, Learning Realistic Human Actions from Movies.
CVPR 2008
3D Reconstruction: Building Rome in a Day
Computational Photography
Colosseum
Image deblurring
S. Cho, S. Lee, Fast Motion Deblurring. SIGGRAPH ASIA, 2009.
Trevi Foundtain
S. Agarwal, N. Snavely, I. Simon, S. M. Seitz and R. Szeliski, Building Rome in a Day, ICCV 2009
23
Image retargeting
M. Rubinstein, A. Shamir, S. Avidan, Multi‐operator Media Retargeting, SIGGRAPH 2009.
Image Stitching
Other Research Directions
• Non‐standard cameras
 Omni‐directional cameras
 Non‐visual sensors
 Camera array, many cameras
• Large‐scale problems
 Using huge database
 Handling a large amount of computation
http://www.ri.cmu.edu/events/sb35/tksuperbowl.html
• Merge with cognitive science and human vision
• Interactions with human
 Vision‐based interface
 Active learning
http://cvlab.epfl.ch/~brown/autostitch/autostitch.html
25
26
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
Related Research Fields
•
•
•
•
•
•
•
•
•
•
Machine learning
Computer graphics
Image processing
Robotics
Multimedia
Optimization
Algorithms
Pattern recognition
Human computer interactions
…
27
CSED441: Introduction to Computer Vision
by Prof. Bohyung Han, Spring 2015
28