'Corresponding points' diaporamas de présentation

Interest Point Detectors (see CS485/685 notes for more details)

Interest Point Detectors (see CS485/685 notes for more details). CS491Y/691Y Topics in Computer Vision Dr. George Bebis. What is an Interest Point?. A point in an image which has a well-defined position and can be robustly detected.

937 views • 76 slides

3D reconstruction Class 16

3D reconstruction Class 16 3D photography course schedule Papers http://www.unc.edu/courses/2004fall/comp/290b/089/papers/ Ideas for a project? 3D modeling Aligning range images Pairwise Globally Surface reconstruction Single range image Merged

1k views • 63 slides

3D reconstruction Class 16

3D reconstruction Class 16. 3D photography course schedule. Papers. http://www.unc.edu/courses/2004fall/comp/290b/089/papers/. Ideas for a project?. 3D modeling. Aligning range images Pairwise Globally Surface reconstruction Single range image Merged.

749 views • 63 slides

Image Stitching

Image Stitching. Ali Farhadi CSE 455 Several slides from Rick Szeliski , Steve Seitz, Derek Hoiem , and Ira Kemelmacher. Combine two or more overlapping images to make one larger image. Add example. Slide credit: Vaibhav Vaish. How to do it?. Basic Procedure

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Peds and Paths: Small Group Behavior in Urban Environments

Peds and Paths: Small Group Behavior in Urban Environments. Joseph K. Kearney Hongling Wang Terry Hostetler Kendall Atkinson The University of Iowa. Pedestrian Activity in Urban Environments. Couples walking down a sidewalk Families window shopping Commuters queuing at a bus stop

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Projector Calibration of Interactive Multi-Resolution Display Systems

??????????????????. Projector Calibration of Interactive Multi-Resolution Display Systems. Presenter: ???. Advisor: ??? ??. Outline. Introduction Related Work Calibration of Interactive Tabletop Displays Calibration of Interactive Cylindrical Displays Experiments and Error Analysis

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Stereo

Stereo. Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1. image 2. Dense depth map. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image.

610 views • 44 slides

Stereo

Stereo. Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Where does the depth information come from?. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1.

567 views • 44 slides

Stereo

Stereo. Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Where does the depth information come from?. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1.

564 views • 43 slides

Camera Calibration

CS 636 Computer Vision. Camera Calibration. Nathan Jacobs. overview. assignment 2 out review from last time review camera parameters intrinsic extrinsic several methods for camera calibration constraints vs. solution methods vs. error functions.

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Structure from motion

Structure from motion. Structure from motion. Given a set of corresponding points in two or more images, compute the camera parameters and the 3D point coordinates. ?. ?. Camera 1. ?. Camera 3. ?. Camera 2. R 1 ,t 1. R 3 ,t 3. R 2 ,t 2. Slide credit: Noah Snavely. X j. x 1 j.

621 views • 41 slides

Modeling the Shape of People from 3D Range Scans

Modeling the Shape of People from 3D Range Scans. Dragomir Anguelov AI Lab Stanford University Joint work with Praveen Srinivasan, Hoi-Cheung Pang, Daphne Koller, Sebastian Thrun . The Dataset. Scans 4 views, ~125k polygons ~65k points each Problems Missing surface

545 views • 40 slides

Going Back a little

Going Back a little. Cameras.ppt. Applications of RANSAC: Solution for affine parameters. Affine transform of [x,y] to [u,v]: Rewrite to solve for transform parameters:. Assignment. Program-1 info -Link Data. Note: You can generate, bring-in, your own images from www, as long as:

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Depth and Size Perception

1.03k views • 32 slides

Multi-view geometry

Multi-view geometry. Multi-view geometry problems. Structure: Given projections of the same 3D point in two or more images, compute the 3D coordinates of that point. ?. Camera 1. Camera 3. Camera 2. R 1 ,t 1. R 3 ,t 3. R 2 ,t 2. Slide credit: Noah Snavely. Multi-view geometry problems.

632 views • 30 slides

Murder most foul

Done by: Group 16 Ong Jie Hao (20) Lim Jingkai (15) Dickson Lim(14) Thio Teng Kiat (26). Murder most foul. Introduction. Solving the crime Evidence The Verdict Reflections. Solving the crime. Most difficult task: To find a link from the various evidences

351 views • 22 slides

Pose Estimation

Pose Estimation. 2010. 3. 16. TUE. Kim Kyungkoo. Active Grasp. Contents. Introduction Pose Estimation Object modeling with features Real-time pose estimation Demo Future works. Introduction. Importance of object recognition and pose estimation. Pose Estimation. Problem Definition

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Sparse Surface Adjustment

336 views • 16 slides

Reflections

248 views • 14 slides

4-1 Congruent Figures

4-1 Congruent Figures. *I CAN name corresponding points of congruent figures. *I CAN use the Third Angle Theorem to prove triangles are equal. F. F. T. K. 35. 62. S. V. M. 35. 83. 62. 83. 83. 6. 74. 44. 62. Objective Practices. Lesson Check: pg. 221 #1-7

98 views • 9 slides