Homographies and Mosaics

Similar documents
Homographies and Mosaics

Image stitching. Image stitching. Video summarization. Applications of image stitching. Stitching = alignment + blending. geometrical registration

Panoramas. CS 178, Spring Marc Levoy Computer Science Department Stanford University

Panoramas. CS 178, Spring Marc Levoy Computer Science Department Stanford University

Image Mosaicing. Jinxiang Chai. Source: faculty.cs.tamu.edu/jchai/cpsc641_spring10/lectures/lecture8.ppt

Panoramas. CS 178, Spring Marc Levoy Computer Science Department Stanford University

The Camera : Computational Photography Alexei Efros, CMU, Fall 2005

The Camera : Computational Photography Alexei Efros, CMU, Fall 2008

Dual-fisheye Lens Stitching for 360-degree Imaging & Video. Tuan Ho, PhD. Student Electrical Engineering Dept., UT Arlington

Colour correction for panoramic imaging

Photographing Long Scenes with Multiviewpoint

Two strategies for realistic rendering capture real world data synthesize from bottom up

ON THE CREATION OF PANORAMIC IMAGES FROM IMAGE SEQUENCES

Recognizing Panoramas

Single-view Metrology and Cameras

Panoramic imaging. Ixyzϕθλt. 45 degrees FOV (normal view)

Discovering Panoramas in Web Videos

Beacon Island Report / Notes

Synthetic Stereoscopic Panoramic Images

Creating a Panorama Photograph Using Photoshop Elements

Midterm Examination CS 534: Computational Photography

Panoramic Image Mosaics

MIT CSAIL Advances in Computer Vision Fall Problem Set 6: Anaglyph Camera Obscura

Image formation - Cameras. Grading & Project. About the course. Tentative Schedule. Course Content. Students introduction

Reconstructing Virtual Rooms from Panoramic Images

Panoramas. Featuring ROD PLANCK. Rod Planck DECEMBER 29, 2017 ADVANCED

Multi Viewpoint Panoramas

Rectified Mosaicing: Mosaics without the Curl* Shmuel Peleg

A short introduction to panoramic images

Princeton University COS429 Computer Vision Problem Set 1: Building a Camera

Unit 1: Image Formation

Image Processing & Projective geometry

Using Line and Ellipse Features for Rectification of Broadcast Hockey Video

Fast Focal Length Solution in Partial Panoramic Image Stitching

Lecture 7: homogeneous coordinates

MEM: Intro to Robotics. Assignment 3I. Due: Wednesday 10/15 11:59 EST

Capturing Light. The Light Field. Grayscale Snapshot 12/1/16. P(q, f)

3D Viewing. Introduction to Computer Graphics Torsten Möller / Manfred Klaffenböck. Machiraju/Zhang/Möller

Getting into the picture

Projection. Announcements. Müller-Lyer Illusion. Image formation. Readings Nalwa 2.1

Perspective. Cornell CS4620/5620 Fall 2012 Lecture Kavita Bala 1 (with previous instructors James/Marschner)

multiframe visual-inertial blur estimation and removal for unmodified smartphones

Dynamically Reparameterized Light Fields & Fourier Slice Photography. Oliver Barth, 2009 Max Planck Institute Saarbrücken

How do we see the world?

Overview. Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image

Tonemapping and bilateral filtering

How to combine images in Photoshop

Photoshop Elements Hints by Steve Miller

CS354 Computer Graphics Viewing and Projections

Computational Photography

303SPH SPHERICAL VR HEAD

High Performance Imaging Using Large Camera Arrays

Introduction to Panoramic photography. David R. Chung Linn Area Photography Club

Cameras. CSE 455, Winter 2010 January 25, 2010

Movie 10 (Chapter 17 extract) Photomerge

Stitching Panoramas using the GIMP

Anamorphic Art. Never argue with an angle - they re almost never right.

3DUNDERWORLD-SLS v.3.0

Robert Mark and Evelyn Billo

Parallax-Free Long Bone X-ray Image Stitching

Digital Design and Communication Teaching (DiDACT) University of Sheffield Department of Landscape. Adobe Photoshop CS4 INTRODUCTION WORKSHOPS

Cameras. Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017

Panoramic Vision System for an Intelligent Vehicle using. a Laser Sensor and Cameras

CALIBRATION OF OPTICAL SATELLITE SENSORS

Problem Set 3. Assigned: March 9, 2006 Due: March 23, (Optional) Multiple-Exposure HDR Images

Projection. Projection. Image formation. Müller-Lyer Illusion. Readings. Readings. Let s design a camera. Szeliski 2.1. Szeliski 2.

History of projection. Perspective. History of projection. Plane projection in drawing

CS6670: Computer Vision

Projection. Readings. Szeliski 2.1. Wednesday, October 23, 13

The key to a fisheye is the relationship between latitude ø of the 3D vector and radius on the 2D fisheye image, namely a linear one where

Technical information about PhoToPlan

Image Formation III Chapter 1 (Forsyth&Ponce) Cameras Lenses & Sensors

AR 2 kanoid: Augmented Reality ARkanoid

CS535 Fall Department of Computer Science Purdue University

Removing Temporal Stationary Blur in Route Panoramas

Geometry-Based Populated Chessboard Recognition

Building a Real Camera

CS6670: Computer Vision

Objective Quality Assessment Method for Stitched Images

Building a Real Camera. Slides Credit: Svetlana Lazebnik

Agenda. Fusion and Reconstruction. Image Fusion & Reconstruction. Image Fusion & Reconstruction. Dr. Yossi Rubner.

Jordan Sorensen. June Author.. Depi4nfent of Electrical Engineering and Computer Science May 21, 2010

Computer Vision. The Pinhole Camera Model

This talk is oriented toward artists.

Sensors and Sensing Cameras and Camera Calibration

Deblurring. Basics, Problem definition and variants

Announcement A total of 5 (five) late days are allowed for projects. Office hours

Introduction to Computer Vision

Perspective. CS 4620 Lecture Steve Marschner. Cornell CS4620 Spring 2018 Lecture 5

Reading. Angel. Chapter 5. Optional

Step 1. Facebook Twitter Google+ Find us on Facebook. Vectortuts+ How to Create a Curious Owl in Illustrator CS4 Vectortuts+

>>> from numpy import random as r >>> I = r.rand(256,256);

METHODS AND ALGORITHMS FOR STITCHING 360-DEGREE VIDEO

A Geometric Correction Method of Plane Image Based on OpenCV

Cameras for Stereo Panoramic Imaging Λ

CS 465 Prelim 1. Tuesday 4 October hours. Problem 1: Image formats (18 pts)

What will be on the final exam?

High Dynamic Range Imaging

Manfrotto 303plus QTVR Pano Head

4 STUDY OF DEBLURRING TECHNIQUES FOR RESTORED MOTION BLURRED IMAGES

Transcription:

Homographies and Mosaics Jeffrey Martin (jeffrey-martin.com) with a lot of slides stolen from Steve Seitz and Rick Szeliski 15-463: Computational Photography Alexei Efros, CMU, Fall 2011

Why Mosaic? Are you getting the whole picture? Compact Camera FOV = 50 x 35 Slide from Brown & Lowe

Why Mosaic? Are you getting the whole picture? Compact Camera FOV = 50 x 35 Human FOV = 200 x 135 Slide from Brown & Lowe

Why Mosaic? Are you getting the whole picture? Compact Camera FOV = 50 x 35 Human FOV = 200 x 135 Panoramic Mosaic = 360 x 180 Slide from Brown & Lowe

Mosaics: stitching images together virtual wide-angle camera

Naïve Stitching left on top right on top Translations are not enough to align the images

A pencil of rays contains all views real camera synthetic camera Can generate any synthetic camera view as long as it has the same center of projection!

Image reprojection mosaic PP The mosaic has a natural interpretation in 3D The images are reprojected onto a common plane The mosaic is formed on this plane Mosaic is a synthetic wide-angle camera

How to do it? Basic Procedure Take a sequence of images from the same position Rotate the camera about its optical center Compute transformation between second image and first Transform the second image to overlap with the first Blend the two together to create a mosaic If there are more images, repeat but wait, why should this work at all? What about the 3D geometry of the scene? Why aren t we using it?

Image reprojection Basic question How to relate two images from the same camera center? how to map a pixel from PP1 to PP2 Answer Cast a ray through each pixel in PP1 Draw the pixel where that ray intersects PP2 PP2 But don t we need to know the geometry of the two planes in respect to the eye? PP1 Observation: Rather than thinking of this as a 3D reprojection, think of it as a 2D image warp from one image to another

Back to Image Warping Which t-form is the right one for warping PP1 into PP2? e.g. translation, Euclidean, affine, projective Translation Affine Perspective 2 unknowns 6 unknowns 8 unknowns

Homography A: Projective mapping between any two PPs with the same center of projection rectangle should map to arbitrary quadrilateral parallel lines aren t but must preserve straight lines same as: project, rotate, reproject called Homography PP2 wx' wy' w p = * * * * * * H * x * y * 1 To apply a homography H p Compute p = Hp (regular matrix multiply) Convert p from homogeneous to image coordinates PP1

Image warping with homographies image plane in front black area where no pixel maps to image plane below

Image rectification p p To unwarp (rectify) an image Find the homography H given a set of p and p pairs How many correspondences are needed? Tricky to write H analytically, but we can solve for it! Find such H that best transforms points p into p Use least-squares!

Least Squares Example Say we have a set of data points (X1,X1 ), (X2,X2 ), (X3,X3 ), etc. (e.g. person s height vs. weight) We want a nice compact formula (a line) to predict X s from Xs: Xa + b = X We want to find a and b How many (X,X ) pairs do we need? What if the data is noisy? ' 2 2 ' 1 1 X b a X X b a X = + = + = ' 2 ' 1 2 1 1 1 X X b a X X Ax=B =......... 1 1 1 ' 3 ' 2 ' 1 3 2 1 X X X b a X X X overconstrained 2 min B Ax

Solving for homographies wx' wy' w p = Hp a b = d e g h c f i x y 1 Can set scale factor i=1. So, there are 8 unkowns. Set up a system of linear equations: Ah = b where vector of unknowns h = [a,b,c,d,e,f,g,h] T Need at least 8 eqs, but the more the better Solve for h. If overconstrained, solve using least-squares: Can be done in Matlab using \ command see help lmdivide min Ah b 2

Fun with homographies Original image St.Petersburg photo by A. Tikhonov Virtual camera rotations

Analysing patterns and shapes What is the shape of the b/w floor pattern? Homography Slide from Criminisi The floor (enlarged) Automatically rectified floor

Analysing patterns and shapes Automatic rectification From Martin Kemp The Science of Art (manual reconstruction) 2 patterns have been discovered! Slide from Criminisi

Analysing patterns and shapes What is the (complicated) shape of the floor pattern? St. Lucy Altarpiece, D. Veneziano Slide from Criminisi Automatically rectified floor

Analysing patterns and shapes Automatic rectification From Martin Kemp, The Science of Art (manual reconstruction) Slide from Criminisi

Julian Beever: Manual Homographies http://users.skynet.be/j.beever/pave.htm

Holbein, The Ambassadors

Panoramas 1. Pick one image (red) 2. Warp the other images towards it (usually, one by one) 3. blend

changing camera center Does it still work? synthetic PP PP1 PP2

Planar scene (or far away) PP1 PP3 PP2 PP3 is a projection plane of both centers of projection, so we are OK! This is how big aerial photographs are made

Planar mosaic

Programming Project #4 Homographies and Panoramic Mosaics Capture photographs (and possibly video) Might want to use tripod Compute homographies (define correspondences) will need to figure out how to setup system of eqs. (un)warp an image (undo perspective distortion) Produce 3 panoramic mosaics (with blending) Do some of the Bells and Whistles

Bells and Whistles Blending and Compositing use homographies to combine images or video and images together in an interesting (fun) way. E.g. put fake graffiti on buildings or chalk drawings on the ground replace a road sign with your own poster project a movie onto a building wall etc.

Bells and Whistles Capture creative/cool/bizzare panoramas Example from UW (by Brett Allen): Ever wondered what is happening inside your fridge while you are not looking? Capture a 360 panorama (quite tricky talk in next class)

Bells and Whistles Video Panorama Capture two (or more) stationary videos (either from the same point, or of a planar/far-away scene). Compute homography and produce a video mosaic. Need to worry about synchronization (not too hard). e.g. capturing a football game from the sides of the stadium Other interesting ideas? talk to me

From previous year s classes Ben Hollis, 2004 Ben Hollis, 2004 Matt Pucevich, 2004 Eunjeong Ryu (E.J), 2004

Go Explore! Ken Chu, 2004