'Matching points' presentation slideshows

Last Two Lectures

817 views • 66 slides

Multi-linear Systems and Invariant Theory in the Context of Computer Vision and Graphics

Multi-linear Systems and Invariant Theory in the Context of Computer Vision and Graphics Class 4: Mutli-View 3D-from-2D CS329 Stanford University. Amnon Shashua. Material We Will Cover Today. Epipolar Geometry and Fundamental Matrix.

444 views • 44 slides

What are Fingerprints?

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Cataloging 101: The Basics

Cataloging 101: The Basics. Barbara C. Greever Linnea Marshall University of Idaho Library. Session Goals. To gain a very basic understanding of descriptive cataloging (We will not cover subject headings or classification in this session)

635 views • 42 slides

Multi-linear Systems and Invariant Theory in the Context of Computer Vision and Graphics CS329

Multi-linear Systems and Invariant Theory in the Context of Computer Vision and Graphics CS329. Amnon Shashua. Material We Will Cover Today. The structure of 3D->2D projection matrix. The homography matrix. A primer on projective geometry of the plane.

497 views • 38 slides

Light Field Compression Using 2-D Warping and Block Matching

Light Field Compression Using 2-D Warping and Block Matching. Shinjini Kundu Anand Kamat Tarcar EE398A Final Project. Outline. Motivation and Goals Overview of Our Method Results and Analysis Summary Future Work References. Motivation.

377 views • 27 slides

DEVELOPMENT OF A COMPUTER PLATFORM FOR OBJECT 3D RECONSTRUCTION USING COMPUTER VISION TECHNIQUES

319 views • 17 slides

Symmetry

Symmetry. Find points with shape edges using different threshold angles. Clustering based on points. Find matching clusters. Computer symmetry plane. My method. Previous pose normalization. Problems. A better way to do cluster ? How to evaluate matching clusters more properly

285 views • 13 slides

ApartMate

ApartMate. Our Mission.

172 views • 10 slides

Assignment 2

Assignment 2. Creating Panoramas. Step 1: (10 pts) Implement the Harris corner detector. Part a. void GaussianBlurImage ( double *image, int w, int h, double sigma) Same as your function for Assignment 1, but the input is a floating point array of size w*h.

105 views • 10 slides