Computer Vision
description
Transcript of Computer Vision
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Computer Vision
Spring 2012 15-385,-685
Instructor: S. Narasimhan
Wean Hall 5409T-R 10:30am – 11:50am
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A Picture is Worth 100 Words
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A Picture is Worth 10,000 Words
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A Picture is Worth a Million Words
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A Picture is Worth a ...?
Necker’s Cube Reversal
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A Picture is Worth a ...?
Checker Shadow Illusion – [E. H. Adelson]
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A Picture is Worth a ...?
Checker Shadow Illusion – [E. H. Adelson]
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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
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Computer Vision
What we see
What a computer sees
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Computer Vision
What we see
What a computer sees
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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...
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Lighting
Scene
Camera
Computer
Scene Interpretation
Components of a Computer Vision System
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Topics covered
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Image Processing
Fourier TransformSampling, Convolution
Image enhancement Feature detection
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Surface Reflectance
[CURET]
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Lightness and Perception
Checker Shadow Illusion – [E. H. Adelson]
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Understanding Optical Illusions
Which is bigger? Straight Lines?
Spinning Wheels?Dots White? Or Black?
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3D from Shading
Shape from Shading Photometric Stereo
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Cameras and their Optics
Today’s Digital Cameras
The Camera Obscura
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Biological Cameras
Human Eye Mosquito Eye
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Optical Flow
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Tracking
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Binocular Stereo
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Range Scanning and Structured Light
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Microsoft Kinect
IR Camera
RGB Camera
IR LED Emitter
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Statistical Techniques
Least Squares Fitting
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Face detection
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Face Recognition
• Principle Components Analysis (PCA)
• Face Recognition
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Some Recent Trends in Vision
Novel Cameras and Displays
*** Topics change every year
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• 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
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Course Logistics
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• Class Notes (required)
• Text, Robot Vision, B.K.P.Horn, MIT Press (recommended)
• Supplementary Material (papers, tutorials)
Text and Reading
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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 Exam3/20/2012: Binocular Stereo 13/22/2012: Binocular Stereo 2 [Project 3 DUE, Project 4 OUT]3/27/2012: Structured Light and Range Imaging
Course Schedule
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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
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• Basic Linear Algebra, Probability, Calculus Required
• Basic Data structures/Programming knowledge
• No Prior knowledge of Computer Vision Required
Prerequisites
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• 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
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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.