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Computer Vision
Spring 2012 15-385,-685
Instructor: S. Narasimhan
Wean Hall 5409
T-R 10:30am – 11:50am
A Picture is Worth 100 Words
A Picture is Worth 10,000 Words
A Picture is Worth a Million Words
A Picture is Worth a ...?
Necker’s Cube Reversal
A Picture is Worth a ...?
Checker Shadow Illusion – [E. H. Adelson]
A Picture is Worth a ...?
Checker Shadow Illusion – [E. H. Adelson]
Human Vision
• Can do amazing things like:
• Recognize people and objects• Navigate through obstacles• Understand mood in the scene• Imagine stories
• But still is not perfect:
• Suffers from Illusions• Ignores many details• Ambiguous description of the world• Doesn’t care about accuracy of world
Computer Vision
What we see
What a computer sees
Computer Vision
What we see
What a computer sees
What is Computer Vision?
• Inverse Optics
• Intelligent interpretation of Imagery
• Building a Visual Cortex
• No matter what your definition is…
– Vision is hard.
– But is fun...
Lighting
Scene
Camera
Computer
Scene Interpretation
Components of a Computer Vision System
Topics covered
Image Processing
Fourier TransformSampling, Convolution
Image enhancement Feature detection
Surface Reflectance
[CURET]
Lightness and Perception
Checker Shadow Illusion – [E. H. Adelson]
Understanding Optical Illusions
Which is bigger? Straight Lines?
Spinning Wheels?Dots White? Or Black?
3D from Shading
Shape from Shading Photometric Stereo
Cameras and their Optics
Today’s Digital Cameras
The Camera Obscura
Biological Cameras
Human Eye Mosquito Eye
Optical Flow
Tracking
Binocular Stereo
Range Scanning and Structured Light
Range Scanning and Structured Light
Microsoft Kinect
IR Camera
RGB Camera
IR LED Emitter
Statistical Techniques
Least Squares Fitting
Face detection
Face Recognition
• Principle Components Analysis (PCA)
• Face Recognition
Some Recent Trends in Vision
Novel Cameras and Displays
*** Topics change every year
• Graduate Level Computer Vision (Hebert, Fall)
• Computational Photography (Efros, Fall)
• Physics-based methods in Comp Vision (Narasimhan)
• Learning-based methods in Comp. Vision (Efros)
• Geometry-based methods in Comp. Vision (Hebert)
Advanced Related Courses at CMU
Course Logistics
• Class Notes (required)
• Text, Robot Vision, B.K.P.Horn, MIT Press (recommended)
• Supplementary Material (papers, tutorials)
Text and Reading
1/17/2012: Introduction and Course Fundamentals1/19/2012: Matlab Review
PART 1 : Signal and Image Processing1/24/2012 1D Signal Processing1/26/2012: 2D Image Processing [Project 1 OUT]1/31/2012: Image Pyramids and Sampling 2/2/2012: Edge Detection2/7/2012: Hough Transform
PART 2: Physics of the World2/9/2012: Surface appearance and BRDF2/14/2012: Photometric Stereo [Project 1 DUE, Project 2 OUT]2/16/2012: Shape from Shading2/21/2012: Direct and Global Illumination
PART 4 : 3D Geometry2/23/2012: Image Formation and Projection2/28/2012: Motion and Optical Flow3/1/2012: Lucas Kanade Tracking [Project 2 DUE Project 3 OUT]3/6/2012: Midterm Review3/8/2012: Midterm Exam
3/20/2012: Binocular Stereo 13/22/2012: Binocular Stereo 2 [Project 3 DUE, Project 4 OUT]3/27/2012: Structured Light and Range Imaging
Course Schedule
PART 4 : Statistical Techniques3/29/2012: Feature Detection 14/03/2012: Classification 14/05/2012: Classification 24/10/2012: Principle Components Analysis [Project 4 DUE]4/12/2012: Applications of PCA [Project 5 OUT]
[Grad project description due]
PART 6: Trends and Challenges in Vision Research4/17/2012: Image Based Rendering4/24/2012: Novel Cameras and Displays4/26/2012: Optical Illusions5/1/2012: Open challenges in vision research [Project 5 DUE]
5/3/2012: Project presentations by undergraduate students5/8/2012: Project presentations by graduate students [Grad Project 6 DUE]5/13/2012: Final Grades Due
Course Schedule
*** Use as a guide…changes possible
• Basic Linear Algebra, Probability, Calculus Required
• Basic Data structures/Programming knowledge
• No Prior knowledge of Computer Vision Required
Prerequisites
• FIVE Projects – 90 % (15%, 15%, 20%, 20%, 20%)
• ONE Midterm – 10 %
• ONE Extra Project for Graduate Students – 20 %
• Most projects include analytic and programming parts.
• All projects must be done individually.
• Programming Environment – Matlab.
• Projects due before midnight on due-date.
• Written parts due in class on the due-date.
• 3 Late Days for the semester. No more extensions.
• Class attendance – 5 % extra credit
Grading
Office Hours
Narasimhan: Smith Hall 223, By Appointment Email: [email protected]
Supreeth Achar: Wednesdays 6:00pm – 8:00pm Email: [email protected]
Gunhee Kim: Thursdays, Thursdays 6:00pm – 8:00pm Email: [email protected]
• Technical Questions: Post on bboard. TAs will answer.