Introduction to Image Processing and Computer Vision. -- Panoramas and Blending --
|
|
- Mervyn Simon
- 5 years ago
- Views:
Transcription
1 Introduction to Image Processing and Computer Vision -- Panoramas and Blending -- Winter 2013/14 Ivo Ihrke
2 Panoramas
3 Mosaics and Panoramas - Outline - Perspective Panoramas - Hardware-Based - Software-Based (Multiple Photographs) - Image registration - Image blending
4 Why Mosaic? Are you getting the whole picture? Compact Camera FOV = 50 x 35 Slide from Brown & Lowe
5 Why Mosaic? Are you getting the whole picture? Compact Camera FOV = 50 x 35 Human FOV = 200 x 135 Slide from Brown & Lowe
6 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
7 Single vs. Multiple Viewpoint Single-viewpoint Necessary for creating pure perspective images. Many vision algorithms assume pinhole cameras. Images that aren t perspective images look distorted.
8 In the old days of film photography Single-viewpoint Single exposure Standard 35mm film
9 Omnidirectional Catadioptric Cameras catadioptric = mirror + lens system O-360 EyeSee360
10 Images of an Omnidirectional Camera images: CAVE lab
11 Catadioptric System Full Texture K [Kuthirummal 2006]
12 Cata-Fisheye Camera [Krishnan and Nayhar 2008]
13 Catadioptric System Stereo object center object
14 Multi-camera, Single-viewpoint? Immersive Media Dodeca2000 PointGrey Ladybug
15 Lens image circle scanning Medium format lens (6 cm x 6 cm image circle) Manual Scan plate APS-C (2.4 cm x 1.6 cm) camera
16 Perspective Panoramas Registration
17
18 Single Center of Projection 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 why don t we need the 3D geometry?
19 Image Reprojection The images are reprojected onto a common plane The mosaic is formed on this plane Mosaic is a synthetic wide-angle camera mosaic PP
20 A pencil of rays contains all views real camera synthetic camera scene Can generate any synthetic camera view as long as it has the same center of projection!
21 No-parallax point Same center of projection can be ensured by rotating camera-lens setup around the entrance pupil (and NOT the nodal point!).
22 Image reprojection How to relate two images from the same camera center? Images contain the same information along the same ray. Use 2D image warp
23 Taxonomy of Projective Transformations
24 Perspective Transformation 3D to 2D projection Point in world coordinates P(x e,y e,z e ) Distance center of projection image plane D(=f) Image coordinates (x s,y s )
25 Homogeneous Coordinates: Point Representation x=(x,y,w) (x,y ) w=1 x' = x w y' = y w
26 Homogeneous Coordinates: Point Representation
27 Hom. Coord.: Line Representation l=(a,b,c) ax+by+c=0
28 Hom. Coordinates: Point on Line x=(x,y,w) l=(a,b,c) xl 0
29 Hom. Coordinates: Intersection of Lines l l x l'l x
30 Hom. Coordinates: Line through 2 Points l=(a,b,c) x=(x,y,w) x =(x,y,w ) x'x l
31 Embedding of R 2 into P 2 For the time being Representation of transformations by 3x3 matrices Mathematical trick convenient representation to express rotations and translations as matrix multiplications Easy to find line through points, point-line/line-line intersections Easy representation of projective transformation (homography) Homogeneous Coordinates for 2D W Y W X W Y X y x y x R / / and, P 1 2 2
32 Projective Transformations Projecting one plane onto another using one projection center
33 Examples of Projective Transformations Projection between 2 images via a world plane Concatenating two projective transforms gives another projective transform Projection between 2 images with the same camera center Rotating camera or camera with varying focal length Shadow projection of a plane onto another plane
34 Taxonomy of Projective Transformations
35 Taxonomy of Projective Transformations
36 Distortions under Central Projection Similarity: circle remains circle, square remains square line orientation is preserved Affine: circle becomes ellipse, square becomes rhombus parallel lines remain parallel Projective: imaged object size depends on distance from camera parallel lines converge
37 Removing Projective Distortion Projective transformation in inhomogeneous form 4 general point correspondences (x,y ->x,y ) on the planar facade lead to eight linear equations of the type Sufficient to solve for H up to multiplicative factor
38 The Direct Linear Transform (DLT) Algorithm Given: 4 2D point correspondences Objective: estimate the projective transform matrix H x x x x i ' ' ' ' x x x x i
39 The DLT Algorithm II Re-phrasing H Re-ording into h vector gives 0 Estimating matrix H from point correspondences is equivalent to i i i w y x x i ' ' ' ' i i i w y x x i i i i w y x h h h h h h h h h
40 The DLT Algorithm III Only rows 1 and 2 are linearly independent omit row 3 Inhomogeneous solution: set one matrix entry equal to 1 (e.g. h33) Solve by Gaussian elimination or least-squares techniques i i i w y x x i ' ' ' ' i i i w y x x i
41 Estimating Homographies
42 Homography or not? Coincidences between 3D points at different depths are preserved Pure camera rotation about camera center 2D Homography Different depths are imaged to different image positions Camera rotates and translates Motion Parallax, no Homography
43 Panoramic Mosaicing Rotation about camera center: homography choose one image as reference compute homography to map neighboring image to reference image plane projectively warp image, add to reference plane repeat for all images bow tie shape
44 Alternative Panoramas Project images onto different surfaces: Cylindrical Spherical Cubic (think of cube map) Images
45 Example My former office Register left and right image to the middle one using two homographies
46 Example My former office all images registered to the central one (2 homographies)
47 Example My former office ghost seams
48 Image Blending
49 Linear Blending «Moyenne» entre deux images Pas la moyenne de l image des objets mais une image de la moyenne des objets et une moyenne évoluant au cours du temps. Comment savoir ce qu est la bonne moyenne? On n en sait rien! Mais les artistes peuvent nous aider 49
50 Fondu «cross-dissolve» Interpolation de l image complète I t = (1-t) * I 1 + t * I 2 Mais que se passe-t-il si les images ne sont pas alignées? 50
51 Aligner puis faire le fondu
52 Image Blending slides from Alexei Efros
53 Feathering = Blending
54 Effect of Window Size 1 left 1 0 right 0
55 Effect of Window Size
56 Good Window Size 1 0 Optimal Window: smooth but not ghosted
57 What is the Optimal Window? To avoid seams window >= size of largest prominent feature To avoid ghosting window <= 2*size of smallest prominent feature Natural to cast this in the Fourier domain largest frequency <= 2*size of smallest frequency do blending in different frequency bands
58 Bandpass Computations
59 Bandpass Computations filtered images Fourier space filter shape Octave = doubling frequency low-pass 1 st octave 2 nd octave 3 rd octave
60 Lowpass
61 First Octave
62 Second Octave
63 Third Octave
64 Zoom-In 3 rd Octave - Jpeg-Artifacts
65 Reconstruction filtered images = low-pass 1 st octave 2 nd octave 3 rd octave original Fourier space filter shape = low-pass 1 st octave 2 nd octave 3 rd octave full freq. range
66 Spatial Domain Interpretation / Implementation original
67 What does blurring take away? smoothed (5x5 Gaussian)
68 High-Pass Filter smoothed minus original
69 Image Pyramids mipmap or precursor of wavelets Gaussian Pyramid
70 Create by Image Sub-sampling 1/8 1/4 Throw away every other row and column to create a 1/2 size image
71 Improper Image Sub-sampling 1/2 1/4 (2x zoom) 1/8 (4x zoom) Why does this look so bad? Aliasing!
72 Proper Sub-Sampling First, band-limit, then sub-sample! Repeat Filter Subsample Until minimum resolution reached filter mask Whole pyramid is only 4/3 the size of the original image!
73 Implementation by Gaussian pre-filtering G 1/8 G 1/4 Gaussian 1/2 Filter size should double for each ½ size reduction.
74 Subsampling with Gaussian pre-filtering Gaussian 1/2 G 1/4 G 1/8 Solution: filter the image, then subsample Filter size should double for each ½ size reduction.
75 Compare with... 1/2 1/4 (2x zoom) 1/8 (4x zoom)
76 Band-pass filtering Gaussian Pyramid (low-pass images) Laplacian Pyramid (subband images) Created from Gaussian pyramid by subtraction
77 Laplacian Pyramid Original image Need this! How can we reconstruct (collapse) this pyramid into the original image?
78 Laplacian Pyramid Need this!
79 Pyramid Blending Left pyramid blend Right pyramid
80 Blending Apples and Oranges original apple original orange blend scale 1 blend scale 2 blend scale 3 pyramid blending
81 Blending Apples and Oranges blend scale 1 pyramid blending
82 Blending Apples and Oranges blend scale 2 pyramid blending
83 Blending Apples and Oranges blend scale 3 pyramid blending
84 Different Frequency Bands
85 Simplification: Two-band Blending Brown & Lowe, 2003 Only use two bands: high freq. and low freq. Blends low freq. smoothly Blend high freq. with no smoothing: use binary mask
86 2-band Blending Low frequency (l > 2 pixels) High frequency (l < 2 pixels)
87 Linear Blending
88 2-band Blending
89 Still Some Artifacts Left Ghosting objects move in the scene. Differing exposures between images. Pyramid blending does not solve this.
90 De-Ghosting In regions with differences don t blend - crop. [Uyttendaele et al. 2001]
91 Gradient Domain Blending In Pyramid Blending, we decomposed our image into 2 nd derivatives (Laplacian) and a low-res image Let us now look at 1 st derivatives (gradients): No need for low-res image captures everything (up to a constant) easy to deal with low-frequency differences Idea: Differentiate Blend Reintegrate
92 Poisson Image Editing original mask Poisson Inpainting result
93 Poisson Image Editing original original to paste copy and paste Poisson Image Editing result
94 Gradient Domain Blending (2D) Take partial derivatives dx and dy (the gradient field) Fiddle around with them (copy, smooth, blend, feather, etc) Reintegrate But now integral(dx) might not equal integral(dy) Find the most agreeable solution Equivalent to solving Poisson equation
95 Gradient Domain Blending (2D) - But now integral(dx) might not equal integral(dy): INCONSISTENCY - There is no UNIQUE SOLUTON! - Poisson-solver (most widely used) can produce artifacts. This is how it looks like when we directly integrate an inconsistent gradient field (row-by-row in this case) +10 ¹ +70
96 Comparisons [Levin et al 2004]
97 End
98 Acknowledgements Many slides by Steve Seitz, Rick Szeliski Histogram slides by Samir H. Abdul-Jauwad Some histogram-matching results by Paul Bourke More slides by Pierre Bénard, Hendrik Lensch
99 The End
Targil 10 : Why Mosaic? Why is this a challenge? Exposure differences Scene illumination Miss-registration Moving objects
Why Mosaic? Are you getting the whole picture? Compact Camera FOV = 5 x 35 Targil : Panoramas - Stitching and Blending Some slides from Alexei Efros 2 Slide from Brown & Lowe Why Mosaic? Are you getting
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 7: Image Alignment and Panoramas What s inside your fridge? http://www.cs.washington.edu/education/courses/cse590ss/01wi/ Projection matrix intrinsics projection
More informationImage Compositing and Blending
Computational Photography and Capture: Image Compositing and Blending Gabriel Brostow & Tim Weyrich TA: Frederic Besse Vignetting 3 Figure from http://www.vanwalree.com/optics/vignetting.html Radial Distortion
More informationAnnouncements. Mosaics. How to do it? Image Mosaics
Announcements Mosaics Project artifact voting Project 2 out today (help session at end of class) http://www.destination36.com/start.htm http://www.vrseattle.com/html/vrview.php?cat_id=&vrs_id=vrs38 Today
More informationRecap from Monday. Frequency domain analytical tool computational shortcut compression tool
Recap from Monday Frequency domain analytical tool computational shortcut compression tool Fourier Transform in 2d in Matlab, check out: imagesc(log(abs(fftshift(fft2(im))))); Image Blending (Szeliski
More informationMosaics. Today s Readings
Mosaics VR Seattle: http://www.vrseattle.com/ Full screen panoramas (cubic): http://www.panoramas.dk/ Mars: http://www.panoramas.dk/fullscreen3/f2_mars97.html Today s Readings Szeliski and Shum paper (sections
More informationImage Warping and Mosacing
Image Warping and Mosacing 15-463: Rendering and Image Processing Alexei Efros with a lot of slides stolen from Steve Seitz and Rick Szeliski Today Mosacs Image Warping Homographies Programming Assignment
More informationMore Mosaic Madness. CS194: Image Manipulation & Computational Photography. Steve Seitz and Rick Szeliski. Jeffrey Martin (jeffrey-martin.
More Mosaic Madness Jeffrey Martin (jeffrey-martin.com) CS194: Image Manipulation & Computational Photography with a lot of slides stolen from Alexei Efros, UC Berkeley, Fall 2018 Steve Seitz and Rick
More informationAnnouncements. Mosaics. Image Mosaics. How to do it? Basic Procedure Take a sequence of images from the same position =
Announcements Project 2 out today panorama signup help session at end of class Today mosaic recap blending Mosaics Full screen panoramas (cubic): http://www.panoramas.dk/ Mars: http://www.panoramas.dk/fullscreen3/f2_mars97.html
More informationImage warping and stitching
Image warping and stitching May 4 th, 2017 Yong Jae Lee UC Davis Last time Interactive segmentation Feature-based alignment 2D transformations Affine fit RANSAC 2 Alignment problem In alignment, we will
More informationToday s lecture. Image Alignment and Stitching. Readings. Motion models
Today s lecture Image Alignment and Stitching Computer Vision CSE576, Spring 2005 Richard Szeliski Image alignment and stitching motion models cylindrical and spherical warping point-based alignment global
More informationImage warping and stitching
Image warping and stitching May 5 th, 2015 Yong Jae Lee UC Davis PS2 due next Friday Announcements 2 Last time Interactive segmentation Feature-based alignment 2D transformations Affine fit RANSAC 3 Alignment
More informationMidterm Examination CS 534: Computational Photography
Midterm Examination CS 534: Computational Photography November 3, 2016 NAME: Problem Score Max Score 1 6 2 8 3 9 4 12 5 4 6 13 7 7 8 6 9 9 10 6 11 14 12 6 Total 100 1 of 8 1. [6] (a) [3] What camera setting(s)
More information6.098 Digital and Computational Photography Advanced Computational Photography. Panoramas. Bill Freeman Frédo Durand MIT - EECS
6.098 Digital and Computational Photography 6.882 Advanced Computational Photography Panoramas Bill Freeman Frédo Durand MIT - EECS Lots of slides stolen from Alyosha Efros, who stole them from Steve Seitz
More informationImage stitching. Digital Visual Effects Yung-Yu Chuang. with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac
Image stitching Digital Visual Effects Yung-Yu Chuang with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac Image stitching Stitching = alignment + blending geometrical registration
More informationHomographies and RANSAC
Homographies and RANSAC Computer vision 6.869 Bill Freeman and Antonio Torralba March 30, 2011 Homographies and RANSAC Homographies RANSAC Building panoramas Phototourism 2 Depth-based ambiguity of position
More informationPanoramas. Why Mosaic? Why Mosaic? Mosaics: stitching images together. Why Mosaic? Olivier Gondry. Bill Freeman Frédo Durand MIT - EECS
Olivier Gondry 6.098 Digital and Computational Photography 6.882 Advanced Computational Photography Panoramas Director of music video and commercial Special effect specialist (Morphing, rotoscoping) Today
More informationImage Stitching. Slides from Rick Szeliski, Steve Seitz, Derek Hoiem, Ira Kemelmacher, Ali Farhadi
Image Stitching Slides from Rick Szeliski, Steve Seitz, Derek Hoiem, Ira Kemelmacher, Ali Farhadi Combine two or more overlapping images to make one larger image Add example Slide credit: Vaibhav Vaish
More informationRecap. DoF Constraint Solver. translation. affine. homography. 3D rotation
Image Blending Recap DoF Constraint Solver translation affine homography 3D rotation Recap DoF Constraint Solver translation 2 affine homography 3D rotation Recap DoF Constraint Solver translation 2 affine
More informationCS4670: Computer Vision
CS467: Computer Vision Noah Snavely Lecture 13: Projection, Part 2 Perspective study of a vase by Paolo Uccello Szeliski 2.1.3-2.1.6 Reading Announcements Project 2a due Friday, 8:59pm Project 2b out Friday
More informationStitching and Blending
Stitching and Blending Kari Pulli VP Computational Imaging Light First project Build your own (basic) programs panorama HDR (really, exposure fusion) The key components register images so their features
More informationCOSC579: Scene Geometry. Jeremy Bolton, PhD Assistant Teaching Professor
COSC579: Scene Geometry Jeremy Bolton, PhD Assistant Teaching Professor Overview Linear Algebra Review Homogeneous vs non-homogeneous representations Projections and Transformations Scene Geometry The
More informationImage stitching. Announcements. Outline. Image stitching
Announcements Image stitching Project #1 was due yesterday. Project #2 handout will be available on the web later tomorrow. I will set up a webpage for artifact voting soon. Digital Visual Effects, Spring
More informationImage stitching. Digital Visual Effects Yung-Yu Chuang. with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac
Image stitching Digital Visual Effects Yung-Yu Chuang with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac Image stitching Stitching = alignment + blending geometrical registration
More informationPhotometric Processing
Photometric Processing 1 Histogram Probability distribution of the different grays in an image 2 Contrast Enhancement Limited gray levels are used Hence, low contrast Enhance contrast 3 Histogram Stretching
More informationImage warping and stitching
Image warping and stitching Thurs Oct 15 Last time Feature-based alignment 2D transformations Affine fit RANSAC 1 Robust feature-based alignment Extract features Compute putative matches Loop: Hypothesize
More informationImage Blending and Compositing NASA
Image Blending and Compositing NASA CS194: Image Manipulation & Computational Photography Alexei Efros, UC Berkeley, Fall 2016 Image Compositing Compositing Procedure 1. Extract Sprites (e.g using Intelligent
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 FDH 204 Lecture 12 130228 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Panoramas, Mosaics, Stitching Two View Geometry
More informationcalibrated coordinates Linear transformation pixel coordinates
1 calibrated coordinates Linear transformation pixel coordinates 2 Calibration with a rig Uncalibrated epipolar geometry Ambiguities in image formation Stratified reconstruction Autocalibration with partial
More informationN-Views (1) Homographies and Projection
CS 4495 Computer Vision N-Views (1) Homographies and Projection Aaron Bobick School of Interactive Computing Administrivia PS 2: Get SDD and Normalized Correlation working for a given windows size say
More informationFeature Matching and RANSAC
Feature Matching and RANSAC Recognising Panoramas. [M. Brown and D. Lowe,ICCV 2003] [Brown, Szeliski, Winder, CVPR 2005] with a lot of slides stolen from Steve Seitz, Rick Szeliski, A. Efros Introduction
More informationMultiresolution Image Processing
Multiresolution Image Processing 2 Processing and Analysis of Images at Multiple Scales What is Multiscale Decompostion? Why use Multiscale Processing? How to use Multiscale Processing? Related Concepts:
More informationSingle-view 3D Reconstruction
Single-view 3D Reconstruction 10/12/17 Computational Photography Derek Hoiem, University of Illinois Some slides from Alyosha Efros, Steve Seitz Notes about Project 4 (Image-based Lighting) You can work
More informationVision Review: Image Formation. Course web page:
Vision Review: Image Formation Course web page: www.cis.udel.edu/~cer/arv September 10, 2002 Announcements Lecture on Thursday will be about Matlab; next Tuesday will be Image Processing The dates some
More informationVideo Mosaics for Virtual Environments, R. Szeliski. Review by: Christopher Rasmussen
Video Mosaics for Virtual Environments, R. Szeliski Review by: Christopher Rasmussen September 19, 2002 Announcements Homework due by midnight Next homework will be assigned Tuesday, due following Tuesday.
More informationGeometric camera models and calibration
Geometric camera models and calibration http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 13 Course announcements Homework 3 is out. - Due October
More informationBroad field that includes low-level operations as well as complex high-level algorithms
Image processing About Broad field that includes low-level operations as well as complex high-level algorithms Low-level image processing Computer vision Computational photography Several procedures and
More informationPanoramic Image Stitching
Mcgill University Panoramic Image Stitching by Kai Wang Pengbo Li A report submitted in fulfillment for the COMP 558 Final project in the Faculty of Computer Science April 2013 Mcgill University Abstract
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 5: Projection Reading: Szeliski 2.1 Projection Reading: Szeliski 2.1 Projection Müller Lyer Illusion http://www.michaelbach.de/ot/sze_muelue/index.html Modeling
More informationThere are many cues in monocular vision which suggests that vision in stereo starts very early from two similar 2D images. Lets see a few...
STEREO VISION The slides are from several sources through James Hays (Brown); Srinivasa Narasimhan (CMU); Silvio Savarese (U. of Michigan); Bill Freeman and Antonio Torralba (MIT), including their own
More informationModeling Light. On Simulating the Visual Experience
Modeling Light 15-463: Rendering and Image Processing Alexei Efros On Simulating the Visual Experience Just feed the eyes the right data No one will know the difference! Philosophy: Ancient question: Does
More informationDD2423 Image Analysis and Computer Vision IMAGE FORMATION. Computational Vision and Active Perception School of Computer Science and Communication
DD2423 Image Analysis and Computer Vision IMAGE FORMATION Mårten Björkman Computational Vision and Active Perception School of Computer Science and Communication November 8, 2013 1 Image formation Goal:
More informationImage-Based Rendering
Image-Based Rendering COS 526, Fall 2016 Thomas Funkhouser Acknowledgments: Dan Aliaga, Marc Levoy, Szymon Rusinkiewicz What is Image-Based Rendering? Definition 1: the use of photographic imagery to overcome
More informationCSE 252B: Computer Vision II
CSE 252B: Computer Vision II Lecturer: Serge Belongie Scribe: Jayson Smith LECTURE 4 Planar Scenes and Homography 4.1. Points on Planes This lecture examines the special case of planar scenes. When talking
More informationImage Formation. Antonino Furnari. Image Processing Lab Dipartimento di Matematica e Informatica Università degli Studi di Catania
Image Formation Antonino Furnari Image Processing Lab Dipartimento di Matematica e Informatica Università degli Studi di Catania furnari@dmi.unict.it 18/03/2014 Outline Introduction; Geometric Primitives
More informationIndex. 3D reconstruction, point algorithm, point algorithm, point algorithm, point algorithm, 253
Index 3D reconstruction, 123 5+1-point algorithm, 274 5-point algorithm, 260 7-point algorithm, 255 8-point algorithm, 253 affine point, 43 affine transformation, 55 affine transformation group, 55 affine
More informationFiltering Applications & Edge Detection. GV12/3072 Image Processing.
Filtering Applications & Edge Detection GV12/3072 1 Outline Sampling & Reconstruction Revisited Anti-Aliasing Edges Edge detection Simple edge detector Canny edge detector Performance analysis Hough Transform
More informationObject Recognition with Invariant Features
Object Recognition with Invariant Features Definition: Identify objects or scenes and determine their pose and model parameters Applications Industrial automation and inspection Mobile robots, toys, user
More informationAgenda. Rotations. Camera models. Camera calibration. Homographies
Agenda Rotations Camera models Camera calibration Homographies D Rotations R Y = Z r r r r r r r r r Y Z Think of as change of basis where ri = r(i,:) are orthonormal basis vectors r rotated coordinate
More information11/28/17. Midterm Review. Magritte, Homesickness. Computational Photography Derek Hoiem, University of Illinois
Midterm Review 11/28/17 Computational Photography Derek Hoiem, University of Illinois Magritte, Homesickness Major Topics Linear Filtering How it works Template and Frequency interpretations Image pyramids
More informationIndex. 3D reconstruction, point algorithm, point algorithm, point algorithm, point algorithm, 263
Index 3D reconstruction, 125 5+1-point algorithm, 284 5-point algorithm, 270 7-point algorithm, 265 8-point algorithm, 263 affine point, 45 affine transformation, 57 affine transformation group, 57 affine
More informationCamera Calibration. Schedule. Jesus J Caban. Note: You have until next Monday to let me know. ! Today:! Camera calibration
Camera Calibration Jesus J Caban Schedule! Today:! Camera calibration! Wednesday:! Lecture: Motion & Optical Flow! Monday:! Lecture: Medical Imaging! Final presentations:! Nov 29 th : W. Griffin! Dec 1
More informationCS 664 Slides #9 Multi-Camera Geometry. Prof. Dan Huttenlocher Fall 2003
CS 664 Slides #9 Multi-Camera Geometry Prof. Dan Huttenlocher Fall 2003 Pinhole Camera Geometric model of camera projection Image plane I, which rays intersect Camera center C, through which all rays pass
More informationImage Based Rendering. D.A. Forsyth, with slides from John Hart
Image Based Rendering D.A. Forsyth, with slides from John Hart Topics Mosaics translating cameras reveal extra information, break occlusion Optical flow for very small movements of the camera Explicit
More informationCameras and Stereo CSE 455. Linda Shapiro
Cameras and Stereo CSE 455 Linda Shapiro 1 Müller-Lyer Illusion http://www.michaelbach.de/ot/sze_muelue/index.html What do you know about perspective projection? Vertical lines? Other lines? 2 Image formation
More informationThe 7d plenoptic function, indexing all light.
Previous Lecture The 7d plenoptic function, indexing all light. Lightfields: a 4d (not 5d!) data structure which captures all outgoing light from a region and permits reconstruction of arbitrary synthetic
More informationImage-based Modeling and Rendering: 8. Image Transformation and Panorama
Image-based Modeling and Rendering: 8. Image Transformation and Panorama I-Chen Lin, Assistant Professor Dept. of CS, National Chiao Tung Univ, Taiwan Outline Image transformation How to represent the
More informationAutomatic Image Alignment
Automatic Image Alignment Mike Nese with a lot of slides stolen from Steve Seitz and Rick Szeliski 15-463: Computational Photography Alexei Efros, CMU, Fall 2010 Live Homography DEMO Check out panoramio.com
More informationMultiple View Geometry
Multiple View Geometry Martin Quinn with a lot of slides stolen from Steve Seitz and Jianbo Shi 15-463: Computational Photography Alexei Efros, CMU, Fall 2007 Our Goal The Plenoptic Function P(θ,φ,λ,t,V
More informationCompositing a bird's eye view mosaic
Compositing a bird's eye view mosaic Robert Laganiere School of Information Technology and Engineering University of Ottawa Ottawa, Ont KN 6N Abstract This paper describes a method that allows the composition
More informationWarping, Morphing and Mosaics
Computational Photograph and Video: Warping, Morphing and Mosaics Prof. Marc Pollefes Dr. Gabriel Brostow Toda s schedule Last week s recap Warping Morphing Mosaics Toda s schedule Last week s recap Warping
More informationComputational Photography
Computational Photography Matthias Zwicker University of Bern Fall 2010 Today Light fields Introduction Light fields Signal processing analysis Light field cameras Application Introduction Pinhole camera
More informationCSE 527: Introduction to Computer Vision
CSE 527: Introduction to Computer Vision Week 5 - Class 1: Matching, Stitching, Registration September 26th, 2017 ??? Recap Today Feature Matching Image Alignment Panoramas HW2! Feature Matches Feature
More informationBut First: Multi-View Projective Geometry
View Morphing (Seitz & Dyer, SIGGRAPH 96) Virtual Camera Photograph Morphed View View interpolation (ala McMillan) but no depth no camera information Photograph But First: Multi-View Projective Geometry
More informationStereo CSE 576. Ali Farhadi. Several slides from Larry Zitnick and Steve Seitz
Stereo CSE 576 Ali Farhadi Several slides from Larry Zitnick and Steve Seitz Why do we perceive depth? What do humans use as depth cues? Motion Convergence When watching an object close to us, our eyes
More informationComputer Vision I - Appearance-based Matching and Projective Geometry
Computer Vision I - Appearance-based Matching and Projective Geometry Carsten Rother 05/11/2015 Computer Vision I: Image Formation Process Roadmap for next four lectures Computer Vision I: Image Formation
More informationFinal Exam Study Guide CSE/EE 486 Fall 2007
Final Exam Study Guide CSE/EE 486 Fall 2007 Lecture 2 Intensity Sufaces and Gradients Image visualized as surface. Terrain concepts. Gradient of functions in 1D and 2D Numerical derivatives. Taylor series.
More informationImage-Based Lighting. Inserting Synthetic Objects
Image-Based Lighting 15-463: Rendering and Image Processing Alexei Efros with a lot of slides donated by Paul Debevec Inserting Synthetic Objects Why does this look so bad? Wrong camera orientation Wrong
More informationCS 2770: Intro to Computer Vision. Multiple Views. Prof. Adriana Kovashka University of Pittsburgh March 14, 2017
CS 277: Intro to Computer Vision Multiple Views Prof. Adriana Kovashka Universit of Pittsburgh March 4, 27 Plan for toda Affine and projective image transformations Homographies and image mosaics Stereo
More informationAutomatic Image Alignment (direct) with a lot of slides stolen from Steve Seitz and Rick Szeliski
Automatic Image Alignment (direct) with a lot of slides stolen from Steve Seitz and Rick Szeliski 15-463: Computational Photography Alexei Efros, CMU, Fall 2005 Today Go over Midterm Go over Project #3
More informationComputer Vision Lecture 17
Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics 13.01.2015 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Announcements Seminar in the summer semester
More informationToday. Stereo (two view) reconstruction. Multiview geometry. Today. Multiview geometry. Computational Photography
Computational Photography Matthias Zwicker University of Bern Fall 2009 Today From 2D to 3D using multiple views Introduction Geometry of two views Stereo matching Other applications Multiview geometry
More informationProjective Geometry and Camera Models
/2/ Projective Geometry and Camera Models Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Note about HW Out before next Tues Prob: covered today, Tues Prob2: covered next Thurs Prob3:
More informationComputer Vision Lecture 17
Announcements Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics Seminar in the summer semester Current Topics in Computer Vision and Machine Learning Block seminar, presentations in 1 st week
More informationAugmented Reality II - Camera Calibration - Gudrun Klinker May 11, 2004
Augmented Reality II - Camera Calibration - Gudrun Klinker May, 24 Literature Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2. (Section 5,
More informationApplication questions. Theoretical questions
The oral exam will last 30 minutes and will consist of one application question followed by two theoretical questions. Please find below a non exhaustive list of possible application questions. The list
More informationAutomatic Image Alignment
Automatic Image Alignment with a lot of slides stolen from Steve Seitz and Rick Szeliski Mike Nese CS194: Image Manipulation & Computational Photography Alexei Efros, UC Berkeley, Fall 2018 Live Homography
More informationIntroduction to Computer Vision. Week 3, Fall 2010 Instructor: Prof. Ko Nishino
Introduction to Computer Vision Week 3, Fall 2010 Instructor: Prof. Ko Nishino Last Week! Image Sensing " Our eyes: rods and cones " CCD, CMOS, Rolling Shutter " Sensing brightness and sensing color! Projective
More informationAutomatic Image Alignment (feature-based)
Automatic Image Alignment (feature-based) Mike Nese with a lot of slides stolen from Steve Seitz and Rick Szeliski 15-463: Computational Photography Alexei Efros, CMU, Fall 2006 Today s lecture Feature
More informationAll good things must...
Lecture 17 Final Review All good things must... UW CSE vision faculty Course Grading Programming Projects (80%) Image scissors (20%) -DONE! Panoramas (20%) - DONE! Content-based image retrieval (20%) -
More informationScene Modeling for a Single View
Scene Modeling for a Single View René MAGRITTE Portrait d'edward James with a lot of slides stolen from Steve Seitz and David Brogan, 15-463: Computational Photography Alexei Efros, CMU, Fall 2005 Classes
More informationCamera Calibration. COS 429 Princeton University
Camera Calibration COS 429 Princeton University Point Correspondences What can you figure out from point correspondences? Noah Snavely Point Correspondences X 1 X 4 X 3 X 2 X 5 X 6 X 7 p 1,1 p 1,2 p 1,3
More informationImage-Based Modeling and Rendering
Image-Based Modeling and Rendering Richard Szeliski Microsoft Research IPAM Graduate Summer School: Computer Vision July 26, 2013 How far have we come? Light Fields / Lumigraph - 1996 Richard Szeliski
More informationComputer Vision CS 776 Fall 2018
Computer Vision CS 776 Fall 2018 Cameras & Photogrammetry 1 Prof. Alex Berg (Slide credits to many folks on individual slides) Cameras & Photogrammetry 1 Albrecht Dürer early 1500s Brunelleschi, early
More informationA Review of Image- based Rendering Techniques Nisha 1, Vijaya Goel 2 1 Department of computer science, University of Delhi, Delhi, India
A Review of Image- based Rendering Techniques Nisha 1, Vijaya Goel 2 1 Department of computer science, University of Delhi, Delhi, India Keshav Mahavidyalaya, University of Delhi, Delhi, India Abstract
More informationAgenda. Rotations. Camera calibration. Homography. Ransac
Agenda Rotations Camera calibration Homography Ransac Geometric Transformations y x Transformation Matrix # DoF Preserves Icon translation rigid (Euclidean) similarity affine projective h I t h R t h sr
More informationLocal Feature Detectors
Local Feature Detectors Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Slides adapted from Cordelia Schmid and David Lowe, CVPR 2003 Tutorial, Matthew Brown,
More informationThe SIFT (Scale Invariant Feature
The SIFT (Scale Invariant Feature Transform) Detector and Descriptor developed by David Lowe University of British Columbia Initial paper ICCV 1999 Newer journal paper IJCV 2004 Review: Matt Brown s Canonical
More informationCS201 Computer Vision Camera Geometry
CS201 Computer Vision Camera Geometry John Magee 25 November, 2014 Slides Courtesy of: Diane H. Theriault (deht@bu.edu) Question of the Day: How can we represent the relationships between cameras and the
More informationSIFT: SCALE INVARIANT FEATURE TRANSFORM SURF: SPEEDED UP ROBUST FEATURES BASHAR ALSADIK EOS DEPT. TOPMAP M13 3D GEOINFORMATION FROM IMAGES 2014
SIFT: SCALE INVARIANT FEATURE TRANSFORM SURF: SPEEDED UP ROBUST FEATURES BASHAR ALSADIK EOS DEPT. TOPMAP M13 3D GEOINFORMATION FROM IMAGES 2014 SIFT SIFT: Scale Invariant Feature Transform; transform image
More informationImage Composition. COS 526 Princeton University
Image Composition COS 526 Princeton University Modeled after lecture by Alexei Efros. Slides by Efros, Durand, Freeman, Hays, Fergus, Lazebnik, Agarwala, Shamir, and Perez. Image Composition Jurassic Park
More information360 Full View Spherical Mosaic
360 Full View Spherical Mosaic Huang Wenfan Huang Yehui Rong Nan U017865B U017844X U018274R Objective Full spherical mosaic 360 x 180. All images are taken with camera mounted on a tripod. Registration
More informationIntroduction to Computer Vision
Introduction to Computer Vision Michael J. Black Nov 2009 Perspective projection and affine motion Goals Today Perspective projection 3D motion Wed Projects Friday Regularization and robust statistics
More informationComputer Vision. Recap: Smoothing with a Gaussian. Recap: Effect of σ on derivatives. Computer Science Tripos Part II. Dr Christopher Town
Recap: Smoothing with a Gaussian Computer Vision Computer Science Tripos Part II Dr Christopher Town Recall: parameter σ is the scale / width / spread of the Gaussian kernel, and controls the amount of
More informationImage-based Lighting
Image-based Lighting 10/17/15 T2 Computational Photography Derek Hoiem, University of Illinois Many slides from Debevec, some from Efros Next week Derek away for ICCV (Venice) Zhizhong and Aditya will
More informationScene Modeling for a Single View
Scene Modeling for a Single View René MAGRITTE Portrait d'edward James CS194: Image Manipulation & Computational Photography with a lot of slides stolen from Alexei Efros, UC Berkeley, Fall 2014 Steve
More informationDense 3D Reconstruction. Christiano Gava
Dense 3D Reconstruction Christiano Gava christiano.gava@dfki.de Outline Previous lecture: structure and motion II Structure and motion loop Triangulation Today: dense 3D reconstruction The matching problem
More informationBlending and Compositing
09/26/17 Blending and Compositing Computational Photography Derek Hoiem, University of Illinois hybridimage.m pyramids.m Project 1: issues Basic tips Display/save Laplacian images using mat2gray or imagesc
More informationBSB663 Image Processing Pinar Duygulu. Slides are adapted from Selim Aksoy
BSB663 Image Processing Pinar Duygulu Slides are adapted from Selim Aksoy Image matching Image matching is a fundamental aspect of many problems in computer vision. Object or scene recognition Solving
More informationCMPSCI 670: Computer Vision! Image formation. University of Massachusetts, Amherst September 8, 2014 Instructor: Subhransu Maji
CMPSCI 670: Computer Vision! Image formation University of Massachusetts, Amherst September 8, 2014 Instructor: Subhransu Maji MATLAB setup and tutorial Does everyone have access to MATLAB yet? EdLab accounts
More information