Stitching Photo Mosaics

Slides:



Advertisements
Similar presentations
Feature Based Image Mosaicing
Advertisements

Summary of Friday A homography transforms one 3d plane to another 3d plane, under perspective projections. Those planes can be camera imaging planes or.
Photo Stitching Panoramas from Multiple Images Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem 02/28/12.
Stitching Photo Mosaics. Stitching photos to construct a wild-view scene. Part1: CORNER DETECTION Part2: PERSPECTIVE MAPPING and MOSAICING Handout after.
Mosaics con’t CSE 455, Winter 2010 February 10, 2010.
Recognising Panoramas
Lecture 8: Geometric transformations CS4670: Computer Vision Noah Snavely.
Direct Methods for Visual Scene Reconstruction Paper by Richard Szeliski & Sing Bing Kang Presented by Kristin Branson November 7, 2002.
Homographies and Mosaics : Computational Photography Alexei Efros, CMU, Fall 2005 © Jeffrey Martin (jeffrey-martin.com) with a lot of slides stolen.
Lecture 7: Image Alignment and Panoramas CS6670: Computer Vision Noah Snavely What’s inside your fridge?
Automatic Panoramic Image Stitching using Local Features Matthew Brown and David Lowe, University of British Columbia.
Lecture 6: Image transformations and alignment CS6670: Computer Vision Noah Snavely.
Lecture 11: Structure from motion CS6670: Computer Vision Noah Snavely.
Computer Vision A Hand-Held “Scanner” for Large-Format Images COMP 256 Adrian Ilie Steps Towards.
Lecture 9: Image alignment CS4670: Computer Vision Noah Snavely
Image Stitching and Panoramas
Automatic Image Stitching using Invariant Features Matthew Brown and David Lowe, University of British Columbia.
1Jana Kosecka, CS 223b Cylindrical panoramas Cylindrical panoramas with some slides from R. Szeliski, S. Seitz, D. Lowe, A. Efros,
Image Warping and Mosacing : Rendering and Image Processing Alexei Efros …with a lot of slides stolen from Steve Seitz and Rick Szeliski.
COS 429 PS3: Stitching a Panorama Due November 4 th.
Announcements Project 1 artifact voting Project 2 out today (help session at end of class)
CSCE 641 Computer Graphics: Image-based Modeling (Cont.) Jinxiang Chai.
Announcements Midterm due Friday, beginning of lecture Guest lecture on Friday: Antonio Criminisi, Microsoft Research.
Image Stitching Ali Farhadi CSE 455
CSC 589 Lecture 22 Image Alignment and least square methods Bei Xiao American University April 13.
Projective geometry of 2-space DLT alg HZ 4.1 Rectification HZ 2.7 Hierarchy of maps Invariants HZ 2.4 Projective transform HZ 2.3 Behaviour at infinity.
Lecture 12: Image alignment CS4670/5760: Computer Vision Kavita Bala
Advanced Computer Vision Feature-based Alignment Lecturer: Lu Yi & Prof. Fuh CSIE NTU.
Image Stitching Shangliang Jiang Kate Harrison. What is image stitching?
IMAGE MOSAICING Summer School on Document Image Processing
Mosaics Today’s Readings Szeliski, Ch 5.1, 8.1 StreetView.
CS654: Digital Image Analysis Lecture 8: Stereo Imaging.
Example: line fitting. n=2 Model fitting Measure distances.
Local invariant features 1 Thursday October 3 rd 2013 Neelima Chavali Virginia Tech.
Feature Matching. Feature Space Outlier Rejection.
Maarten Van Lier 2 e Master Computerwetenschappen.
Image Stitching II Linda Shapiro CSE 455. RANSAC for Homography Initial Matched Points.
Midterm Review. Tuesday, November 3 7:15 – 9:15 p.m. in room 113 Psychology Closed book One 8.5” x 11” sheet of notes on both sides allowed Bring a calculator.
Announcements Final is Thursday, March 18, 10:30-12:20 –MGH 287 Sample final out today.
Lecture 22: Structure from motion CS6670: Computer Vision Noah Snavely.
COS 429 PS3: Stitching a Panorama Due November 10 th.
Image Stitching Computer Vision CS 691E Some slides from Richard Szeliski.
Lecture 16: Image alignment
Image Stitching II Linda Shapiro EE/CSE 576.
Prof. Adriana Kovashka University of Pittsburgh October 3, 2016
CS4670 / 5670: Computer Vision Kavita Bala Lecture 20: Panoramas.
More Mosaic Madness © Jeffrey Martin (jeffrey-martin.com)
CS 4501: Introduction to Computer Vision Sparse Feature Detectors: Harris Corner, Difference of Gaussian Connelly Barnes Slides from Jason Lawrence, Fei.
COSC579: Image Align, Mosaic, Stitch
Announcements Final is Thursday, March 20, 10:30-12:20pm
Lecture 7: Image alignment
More Mosaic Madness : Computational Photography
Image Stitching Slides from Rick Szeliski, Steve Seitz, Derek Hoiem, Ira Kemelmacher, Ali Farhadi.
More Mosaic Madness © Jeffrey Martin (jeffrey-martin.com)
Idea: projecting images onto a common plane
Announcements Project 2 out today (help session at end of class)
Image Stitching II Linda Shapiro EE/CSE 576.
Image Stitching II Linda Shapiro CSE 455.
Image Stitching Computer Vision CS 678
Filtering Things to take away from this lecture An image as a function
Announcements Final is Thursday, March 16, 10:30-12:20
More Mosaic Madness © Jeffrey Martin (jeffrey-martin.com)
Announcements Project 1 artifact voting Project 2 out today
Image Stitching II Linda Shapiro EE/CSE 576.
Automatic Panoramic Image Stitching using Invariant Features
Local features and image matching May 7th, 2019
Image Stitching Linda Shapiro ECE/CSE 576.
Image Stitching II Linda Shapiro ECE P 596.
Image Stitching Linda Shapiro ECE P 596.
Presentation transcript:

Stitching Photo Mosaics IBMR Assignment 1 Stitching Photo Mosaics

What is Photo Mosaic? Stitching photos to construct a wild-view scene. Part1: CORNER DETECTION Part2: PERSPECTIVE MAPPING and MOSAICING Handout after Part2 Finished

PERSPECTIVE MAPPING and MOSAICING

Requirement Read n>2 images, and create an image mosaic by registering, projective warping, resampling, and compositing them. (bonus) multiband blending, SIFT ,panorama or other methods mentioned in class.

Just overlapping

Steps Shoot the Pictures Recover Homographies Warp the Images/ Image Rectification Gain Compensation Blend the images into a mosaic

Shoot the Pictures You may use the photos on the webpage, but shoot your own photos and mosaic them will get bonus credit. Shoot photos as: Overlap the fields of view significantly. 40% to 70% overlap is recommended.

Recover Homographies Construct a linear system as: p’=Hp, where p’ and p are correspondence points. Follow the Lecture 8 page 6~9. You may try Affine mappings(DOF=6) or Projective mappings(DOF=8). Solve Ax=0

Warp the Images/Image Rectification Source scanning(forward mapping) or destination scanning(inverse mapping). You will need to avoid aliasing when resampling the image. Be careful of the size of the resulting image.

Gain Compensation Find the optimize gains of gi according to means of overlapping regions between image pair i and j.

Blend the images into a mosaic Linear blending by the weights: where w(x) varies linearly from 1 at the centre of the image to 0 at the edge. Multi-band blending (bonus): A B 𝐼(𝑥,𝑦)= 𝐼𝑎∗ 𝑊−𝐷𝑎 +𝐼𝑏∗(𝑊−𝐷𝑏) 𝑊−𝐷𝑎 +(𝑊−𝐷𝑏)

Multi-band blending Band 1 scale 0 to σ Band 2 scale σ to 2σ Band 3 lower than 2σ

Support Your own project1a code. A C called matlab library. to calculate inverse matrix , SVD or etc.

Result

Grading Basic: 75% Bonus: Harris Corner Detection + KNN (Hw1a) RANSAC Projection Mapping / Affine Mapping Image Warping Bonus: Non-Maximum Suppression 5% KD Tree 5% SIFT 15% Gain Compensation 10% Linear Blending 5% Multi Blending 10% Stitching your own photos 5% Others

Deadline 11/22 11:59:59pm Upload your program & report to: host : caig.cs.nctu.edu.tw port : 30021 username : IBMR10 password : IBMR10 and create your own folder with your ID.