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Binocular Stereo
Binocular Stereo
Binocular Stereo
Binocular Stereo
Optical Flow
Optical Flow
Optical Flow
Optical Flow
Range Scanning and Structured Light
Range Scanning and Structured Light
Range Scanning and Structured Light
Range Scanning and Structured Light
Range Scanning and Structured Light
Range Scanning and Structured Light
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Visual Cues for 3D
Why do we have two eyes
Why do we have two eyes
Why do we have two eyes
Why do we have two eyes
1. Two is better than one
1. Two is better than one
2. Depth from Convergence
2. Depth from Convergence
2. Depth from Convergence
2. Depth from Convergence
3. Depth from binocular disparity
3. Depth from binocular disparity
Computer Vision
Computer Vision
Stereo Rectification
Stereo Rectification
Stereo Rectification
Stereo Rectification
Size of Matching window
Size of Matching window
Size of Matching window
Size of Matching window
Stereo Results
Stereo Results
Stereo Results
Stereo Results
Better methods exist
Better methods exist
Stereo Example
Stereo Example
Stereo Example
Stereo Example
Stereo Example
Stereo Example
Stereo Example
Stereo Example
Stereo Matching
Stereo Matching
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Computer Vision

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1Computer Vision. Spring 2006 19converging point. C: object nearer
15-385,-685 Instructor: S. Narasimhan Wean projects to the outside of the P,
5403 T-R 3:00pm – 4:20pm Lecture #14. disparity = +. F: object farther projects
2Announcements. Homework 4 went out to the inside of the P, disparity = -.
Tuesday. Due April 4. Start early. Sign and magnitude of disparity.
3Course Schedule – Done! 1/17/2006: 20
Introduction and Course Fundamentals PART 21Disparity and Depth. scene.
1 : Cameras and Imaging 1/19/2006: Image 22Disparity and Depth. scene. inverse
Formation and Projection 1/24/2006: Matlab proportional to depth. disparity increases
Review 1/26/2006: Image Sensing [Homework with baseline b.
1 OUT] PART 2 : Signal and Image 23Vergence. Field of view decreases with
Processing 1/31/2006: Binary Image increase in baseline and vergence Accuracy
Processing 2/2/2006: 1D Signal Processing increases with baseline and vergence.
[Homework 1 DUE; Homework 2 OUT] 2/7/2006: 24Binocular Stereo. scene point. image
2D Image Processing 2/9/2006: Edge plane. optical center.
Detection 2/14/2006: Image Pyramids 25Binocular Stereo. Basic Principle:
2/16/2006: Hough Transform [Homework 2 Triangulation Gives reconstruction as
DUE; Homework 3 OUT] PART 3: Physics of intersection of two rays. Requires
the World 2/21/2006: Basic Principles of calibration point correspondence.
Radiometry 2/23/2006: Retinex Theory 26Stereo Correspondence. Determine Pixel
2/28/2006: Surface Reflectance and BRDF Correspondence Pairs of points that
3/2/2006: Photometric Stereo [Homework 3 correspond to same scene point. Epipolar
DUE] 3/7/2006: Midterm Review 3/9/2006: Constraint Reduces correspondence problem
Midterm Exam 3/13/2006: Midterm Grades Due to 1D search along conjugate epipolar
3/21/2006: Shape from Shading [Homework 4 lines Java demo:
OUT]. http://www.ai.sri.com/~luong/research/Meta
4Course Schedule. PART 4 : 3D Geometry DViewer/EpipolarGeo.html.
3/23/2006: Binocular Stereo 1 3/28/2006: 27Stereo Image Rectification.
Binocular Stereo 2 3/30/2006: Motion and 28Stereo Image Rectification. reproject
Optical Flow 4/4/2006: Line Drawing image planes onto a common plane parallel
[Homework 4 DUE; Homework 5 OUT] 4/6/2006: to the line between optical centers pixel
Structured Light PART 5 : Statistical motion is horizontal after this
Techniques 4/11/2006: Linear Least Squares transformation C. Loop and Z. Zhang.
4/13/2006: Principle Components Analysis Computing Rectifying Homographies for
4/18/2006: Applications of PCA [Homework 5 Stereo Vision. IEEE Conf. Computer Vision
DUE; Homework 6 OUT] PART 6: Current and Pattern Recognition, 1999.
Trends and Challenges in Vision Research 29Stereo Rectification.
4/27/2006: Novel Cameras and Displays 30Basic Stereo Algorithm. compare with
5/2/2006: Open challenges in vision every pixel on same epipolar line in right
research 5/4/2006: Review Class [Homework image. pick pixel with minimum match cost.
6 DUE] 5/9/2006: Final Exam 5/18/2006: 31Size of Matching window. Better
Final Grades Due. *** Use as a results with adaptive window T. Kanade and
guide…changes possible. M. Okutomi, A Stereo Matching Algorithm
5Binocular Stereo. with an Adaptive Window: Theory and
6Optical Flow. Experiment,, Proc. International
7Range Scanning and Structured Light. Conference on Robotics and Automation,
8Binocular Stereo Lecture #14. 1991. D. Scharstein and R. Szeliski.
9Recovering 3D from Images. How can we Stereo matching with nonlinear diffusion.
automatically compute 3D geometry from International Journal of Computer Vision,
images? What cues in the image provide 3D 28(2):155-174, July 1998. Effect of window
information? size. Smaller window Good/bad ? Larger
10Visual Cues for 3D. Shading. Merle window Good/bad ?
Norman Cosmetics, Los Angeles. 32Stereo Results. Data from University
11Visual Cues for 3D. Shading Texture. of Tsukuba. Scene. Ground truth.
The Visual Cliff, by William Vandivert, 33Results with Window Search.
1960. Window-based matching (best window size).
12Visual Cues for 3D. Shading Texture Ground truth.
Focus. From The Art of Photography, Canon. 34Better methods exist... State of the
13Visual Cues for 3D. Shading Texture art method Boykov et al., Fast Approximate
Focus Motion. Energy Minimization via Graph Cuts,
14Visual Cues for 3D. Shading Texture International Conference on Computer
Focus Motion. Others: Highlights Shadows Vision, September 1999. Ground truth.
Silhouettes Inter-reflections Symmetry 35Stereo Example. input image (1 of 2).
Light Polarization ... Shape From X X = 36Stereo Example. H. Tao et al. “Global
shading, texture, focus, motion, ... We’ll matching criterion and color segmentation
focus on the motion cue. based stereo”.
15Stereo Reconstruction. The Stereo 37Stereo Example. H. Tao et al. “Global
Problem Shape from two (or more) images matching criterion and color segmentation
Biological motivation. known camera based stereo”.
viewpoints. 38Stereo Matching. Features vs. Pixels?
16Why do we have two eyes? Cyclope vs. Do we extract features prior to matching?
Odysseus. Julesz-style Random Dot Stereogram.
171. Two is better than one. 39Next Class. Binocular Stereo (relative
182. Depth from Convergence. Human and absolute orientation) Reading: Horn,
performance: up to 6-8 feet. Chapter 13.
193. Depth from binocular disparity. P:
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Computer Vision

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