Project Presentation – Week 6

Slides:



Advertisements
Similar presentations
Feature Detection. Description Localization More Points Robust to occlusion Works with less texture More Repeatable Robust detection Precise localization.
Advertisements

DEVELOPMENT OF A COMPUTER PLATFORM FOR OBJECT 3D RECONSTRUCTION USING COMPUTER VISION TECHNIQUES Teresa C. S. Azevedo João Manuel R. S. Tavares Mário A.
3D Model Matching with Viewpoint-Invariant Patches(VIP) Reporter :鄒嘉恆 Date : 10/06/2009.
Assignment 4 Cloning Yourself
The fundamental matrix F
3D reconstruction.
For Internal Use Only. © CT T IN EM. All rights reserved. 3D Reconstruction Using Aerial Images A Dense Structure from Motion pipeline Ramakrishna Vedantam.
MASKS © 2004 Invitation to 3D vision Lecture 7 Step-by-Step Model Buidling.
Multiple View Reconstruction Class 24 Multiple View Geometry Comp Marc Pollefeys.
Joshua Michalczak For UCF REU in Computer Vision, Summer 2010.
Image Correspondence and Depth Recovery Gene Wang 4/26/2011.
3D reconstruction class 11
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
3D reconstruction of cameras and structure x i = PX i x’ i = P’X i.
Lecture 11: Structure from motion CS6670: Computer Vision Noah Snavely.
Lecture 22: Structure from motion CS6670: Computer Vision Noah Snavely.
Distinctive image features from scale-invariant keypoints. David G. Lowe, Int. Journal of Computer Vision, 60, 2 (2004), pp Presented by: Shalomi.
Planar Matchmove Using Invariant Image Features Andrew Kaufman.
Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi.
CSE 803 Fall 2008 Stockman1 Structure from Motion A moving camera/computer computes the 3D structure of the scene and its own motion.
Previously Two view geometry: epipolar geometry Stereo vision: 3D reconstruction epipolar lines Baseline O O’ epipolar plane.
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2006 with a lot of slides stolen from Steve Seitz and.
Global Alignment and Structure from Motion Computer Vision CSE455, Winter 2008 Noah Snavely.
CSCE 641 Computer Graphics: Image-based Modeling (Cont.) Jinxiang Chai.
Computing transformations Prof. Noah Snavely CS1114
Lecture 12: Structure from motion CS6670: Computer Vision Noah Snavely.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Robust fitting Prof. Noah Snavely CS1114
Automatic Camera Calibration
Computer vision: models, learning and inference
CSC 589 Lecture 22 Image Alignment and least square methods Bei Xiao American University April 13.
MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.
Lecture 12: Image alignment CS4670/5760: Computer Vision Kavita Bala
Lecture 12 Stereo Reconstruction II Lecture 12 Stereo Reconstruction II Mata kuliah: T Computer Vision Tahun: 2010.
The Correspondence Problem and “Interest Point” Detection Václav Hlaváč Center for Machine Perception Czech Technical University Prague
Lecture 4: Feature matching CS4670 / 5670: Computer Vision Noah Snavely.
Structure from Motion Course web page: vision.cis.udel.edu/~cv April 25, 2003  Lecture 26.
CSCE 643 Computer Vision: Structure from Motion
Metrology 1.Perspective distortion. 2.Depth is lost.
Correspondence-Free Determination of the Affine Fundamental Matrix (Tue) Young Ki Baik, Computer Vision Lab.
Multiview Geometry and Stereopsis. Inputs: two images of a scene (taken from 2 viewpoints). Output: Depth map. Inputs: multiple images of a scene. Output:
DIEGO AGUIRRE COMPUTER VISION INTRODUCTION 1. QUESTION What is Computer Vision? 2.
UCF REU: Weeks 1 & 2. Gradient Code Gradient Direction of the Gradient: Calculating theta.
Vehicle Segmentation and Tracking From a Low-Angle Off-Axis Camera Neeraj K. Kanhere Committee members Dr. Stanley Birchfield Dr. Robert Schalkoff Dr.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Fitting image transformations Prof. Noah Snavely CS1114
COS429 Computer Vision =++ Assignment 4 Cloning Yourself.
Visual Odometry David Nister, CVPR 2004
Visual Odometry for Ground Vehicle Applications David Nistér, Oleg Naroditsky, and James Bergen Sarnoff Corporation CN5300 Princeton, New Jersey
3D Reconstruction Using Image Sequence
Line Matching Jonghee Park GIST CV-Lab..  Lines –Fundamental feature in many computer vision fields 3D reconstruction, SLAM, motion estimation –Useful.
Motion Detection Frame 1Frame 2 Anomalous activity.
Robust Estimation Course web page: vision.cis.udel.edu/~cv April 23, 2003  Lecture 25.
Computer Vision Computer Vision based Hole Filling Chad Hantak COMP December 9, 2003.
Lecture 22: Structure from motion CS6670: Computer Vision Noah Snavely.
COS 429 PS3: Stitching a Panorama Due November 10 th.
EE 7730 Parametric Motion Estimation. Bahadir K. Gunturk2 Parametric (Global) Motion Affine Flow.
Lecture 7: Image alignment
Image Primitives and Correspondence
Homography From Wikipedia In the field of computer vision, any
Object recognition Prof. Graeme Bailey
3D reconstruction class 11
Image processing and computer vision
Noah Snavely.
Lecture 23: Structure from motion 2
Lecture 8: Image alignment
AHED Automatic Human Emotion Detection
Lecture 15: Structure from motion
SFNet: Learning Object-aware Semantic Correspondence
Presentation transcript:

Project Presentation – Week 6 Joshua Michalczak For UCF REU in Computer Vision, Summer 2010

Recap – What I’m doing ‘Structure from Motion’ (SfM) Recovery of pixel depth by viewing that point from different locations Typical applications: 3D reconstruction Exploring new uses? Detecting occlusion of environment, etc

Where was I? Feature point detection Week 4 Meeting During CVPR Feature point detection Matching of features across different frames Robust estimation of Fundamental Matrix (F) using RANSAC

The F-Matrix DEMO

Where am I now? Can triangulate to ‘Projective’ accuracy Can “upgrade” the ‘projective’ reconstruction to an ‘affine’ reconstruction

Affine Reconstruction? What we expect: projecting 3D points to image matches original feature point locations (correct for 1st view)

Affine Reconstruction? What’s wrong: Doing the same thing for the 2nd view does not yield correct locations. Incorrect by an affine transformation.

Goal for next week Complete ‘Metric’ reconstruction Port code out of Matlab for real-time demonstration