image warping image mapping image mapping examples
play

Image Warping Image Mapping Image Mapping - Examples Forward - PowerPoint PPT Presentation

Nov 28, 2022 •211 likes •715 views

Image Warping Image Mapping Image Mapping - Examples Forward Mapping Forward Mapping - Disadvantages Example Forward Mapping Original Rotated Zoom In Backward Mapping The Problem: (u,v) are not integers! "# Nearest Neighbor

  1. Image Warping

  2. Image Mapping

  3. Image Mapping - Examples

  4. Forward Mapping

  5. Forward Mapping - Disadvantages

  6. Example – Forward Mapping Original Rotated Zoom In

  7. Backward Mapping The Problem: (u,v) are not integers! 𝑔 "#

  10. Nearest Neighbor

  12. Example - Nearest Neighbor Original Rotated Zoom In

  13. Bi-linear Interpolation • Bi-linear interpolates four closest pixels. • The weight for each pixel is proportional to its distance from the sampling point (x,y)

  14. Bi-linear Interpolation

  15. Bi-linear Interpolation

  16. Bi-linear Interpolation

  17. Bi-linear Interpolation

  18. Example Bi-linear Original Rotated Zoom In

  19. Bi-cubic • Bicubic interpolates 16 closest neighbors (4x4 neighborhood) • The result is much more smooth (u,v)

  20. Bi-cubic Interpolation

  21. Bi-cubic Interpolation

  22. Bi-cubic Interpolation

  23. Example Bi-cubic Original Rotated Zoom In

  24. Nearest Neighbor

  25. Bi-Linear Interpolation

  26. Bi-Cubic Interpolation

  27. Comparison Nearest Neighbor Bi-cubic Bi-linear

  28. Direct Rotation

  29. Shear

  30. Shear

  31. Shear and Scale Operating line by line, faster and simpler filters

  32. Shear a a æ ö æ ö æ ö r x x cos - y sin ç ÷ = ç ÷ = ç ÷ T ç ÷ ç ÷ ç ÷ a + a s y x sin y cos è ø è ø è ø æ ö æ ö æ ö æ ö æ ö r u x x ç ÷ ç ÷ = ç ÷ = ç ÷ = ç ÷ B B A T ç ÷ ç ÷ ç ÷ ç ÷ ç ÷ s v y y è ø è ø è ø è ø è ø æ ö æ ö u x ç ÷ ç ÷ = A ç ÷ ç ÷ v y è ø è ø

  33. Shear A preserve columns æ ö æ ö æ ö u x x ç ÷ ç ÷ ç ÷ = = A ç ÷ ç ÷ ç ÷ v y f(x, y) è ø è ø è ø B preserve rows æ ö æ ö æ ö r u g(u, v) ç ÷ ç ÷ ç ÷ = = B ç ÷ ç ÷ ç ÷ s v v è ø è ø è ø

  34. Shear a a æ ö æ ö æ ö r x x cos - y sin ç ÷ ç ÷ ç ÷ = = T ç ÷ ç ÷ ç ÷ a + a s y x sin y cos è ø è ø è ø æ ö æ ö æ ö æ ö æ ö æ ö r u x x g(x, f(x, y)) ç ÷ ç ÷ ç ÷ ç ÷ ç ÷ ç ÷ = = = = B B A B ç ÷ ç ÷ ç ÷ ç ÷ ç ÷ ç ÷ s v y f(x, y) f(x, y) è ø è ø è ø è ø è ø è ø We get = = a + a f(x, y) s x sin y cos = a - a g(u, v) x cos y sin

  35. Shear = a - a g(u, v) x cos y sin We need to express it in terms of u,v We know that x=u, and = = a + a v f(x, y) x sin y cos We get a a v - x sin v - u sin = = y a a cos cos

  36. Shear We put it all together and get a v - u sin = a a = a - a g(u, v) u cos - sin u sec v tan a cos

  37. Shear At last we get æ ö æ ö x x ç ÷ ç ÷ = A ç ÷ ç ÷ a + a y x sin y cos è ø è ø a - a æ ö æ ö u u sec v tan ç ÷ ç ÷ = B ç ÷ ç ÷ v v è ø è ø

  38. Shear Using a large angle (80 degree)

  39. Shear

  40. Shear

  41. Shear Second pass with Backward Mapping

  42. Shear • Rotate in 90 degree, then use shear with a small angle • We still have a scale factor in the shear which create holes; one solution is to use filter

  43. Shear The other solution is by using three shear transformations a - a - a - a æ ö æ ö æ ö æ ö cos sin 1 tan / 2 1 0 1 tan / 2 ç ÷ ç ÷ ç ÷ ç ÷ = ç ÷ ç ÷ ç ÷ ç ÷ a a a sin cos 0 1 sin 1 0 1 è ø è ø è ø è ø We need Three passes instead of Two. But no scale! Just shift lines!

  44. Shear Two first shears

  45. Shear

  46. Shear

  47. Shear

  48. Shear

  49. Shear

Recommend

Cancelable Iris Biometrics using Block Re-mapping and Image Warping Andreas Uhl Department of

Cancelable Iris Biometrics using Block Re-mapping and Image Warping Andreas Uhl Department of

Cancelable Iris Biometrics using Block Re-mapping and Image Warping Andreas Uhl Department of Computer Sciences University of Salzburg, Austria uhl@cosy.sbg.ac.at http://www.wavelab.at/ Outline Introduction & Motivation Iris

285 views • 16 slides
Image warping/morphing Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2005/3/15 with slides

Image warping/morphing Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2005/3/15 with slides

Image warping/morphing Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2005/3/15 with slides by Richard Szeliski, Steve Seitz and Alexei Efros Outline Images Image warping Image morphing Image fundamentals B A Image

966 views • 60 slides
Image Transforma1ons image filtering : change range of image Image Processing : g(x) =

Image Transforma1ons image filtering : change range of image Image Processing : g(x) =

Image Transforma1ons image filtering : change range of image Image Processing : g(x) = h(f(x)) Transforma1ons, Warping and Morphing h Last Week Courtesy: Irfan Essa, Georgia Tech James Hayes, Georgia Tech Alexei Efros, UC Berkeley

308 views • 6 slides
Automatic 3D Mapping for Infrared Image Analysis i i r r f f m m c c a a d d a a r

Automatic 3D Mapping for Infrared Image Analysis i i r r f f m m c c a a d d a a r

Automatic 3D Mapping for Infrared Image Analysis i i r r f f m m c c a a d d a a r r a a c c h h e e V. Martin, V. Gervaise, V. Moncada, M.H. Aumeunier, M. Firdaouss, J.M. Travere (CEA) S. Devaux (IPP), G. Arnoux (CCFE)

490 views • 33 slides
Today Alignment & warping 2d transformations Forward and inverse image warping

Today Alignment & warping 2d transformations Forward and inverse image warping

Today Alignment & warping 2d transformations Forward and inverse image warping Constructing mosaics Homographies Alignment & Warping Robust fitting with RANSAC Thursday, Oct 9 Kristen Grauman UT-Austin Main

626 views • 14 slides
Realistic Image Synthesis - HDR Capture & Tone Mapping - Philipp Slusallek Karol Myszkowski

Realistic Image Synthesis - HDR Capture & Tone Mapping - Philipp Slusallek Karol Myszkowski

Realistic Image Synthesis - HDR Capture & Tone Mapping - Philipp Slusallek Karol Myszkowski Gurprit Singh Realistic Image Synthesis SS18 HDR Image Capture & Tone Mapping Karol Myszkowski LDR vs HDR Comparison Realistic Image

1.02k views • 63 slides
Realistic Image Synthesis - HDR Capture & Tone Mapping - Philipp Slusallek Karol Myszkowski

Realistic Image Synthesis - HDR Capture & Tone Mapping - Philipp Slusallek Karol Myszkowski

Realistic Image Synthesis - HDR Capture & Tone Mapping - Philipp Slusallek Karol Myszkowski Gurprit Singh Realistic Image Synthesis SS20 HDR Image Capture & Tone Mapping Karol Myszkowski LDR vs HDR Comparison Realistic Image

825 views • 57 slides
IBR Approaches for View Synthesis Image-Based Rendering and Modeling l Image-based

IBR Approaches for View Synthesis Image-Based Rendering and Modeling l Image-based

IBR Approaches for View Synthesis Image-Based Rendering and Modeling l Image-based rendering (IBR): A scene is represented as a l Non-physically based image mapping collection of images u Image morphing l 3D model-based rendering (MBR): A

759 views • 28 slides
Image Pyramids 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University What are image

Image Pyramids 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University What are image

Image Pyramids 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University What are image pyramids used for? Image blending Multi-scale Image compression texture mapping Multi-focus composites Noise removal Hybrid images Multi-scale

218 views • 18 slides
Image Restoration Image Enhancement and Image Restoration both deal with improving images. Image

Image Restoration Image Enhancement and Image Restoration both deal with improving images. Image

Image Restoration Image Enhancement and Image Restoration both deal with improving images. Image Analysis In Image Enhancement, the quality of the improved image was Image Restoration and Reconstruction judged subjectively. In contrast, Image

349 views • 8 slides
IMAGE WARPING Vuong Le Dept. Of ECE University of Illinois ECE 417 Spring 2013 With some

IMAGE WARPING Vuong Le Dept. Of ECE University of Illinois ECE 417 Spring 2013 With some

IMAGE WARPING Vuong Le Dept. Of ECE University of Illinois ECE 417 Spring 2013 With some slides from Alexei Efros, Steve Seitz, Jilin Tu, and Hao Tang Content Introduction Parametric warping Barycentric coordinates

874 views • 48 slides
Image Features Sanja Fidler CSC420: Intro to Image Understanding 1 / 1 Image Features Image

Image Features Sanja Fidler CSC420: Intro to Image Understanding 1 / 1 Image Features Image

Image Features Sanja Fidler CSC420: Intro to Image Understanding 1 / 1 Image Features Image features are useful descriptions of local or global image properties designed (or learned!) to accomplish a certain task You may want to choose di ff

941 views • 70 slides
Realistic Image Synthesis - Density Estimation and Photon Mapping - Philipp Slusallek Karol

Realistic Image Synthesis - Density Estimation and Photon Mapping - Philipp Slusallek Karol

Realistic Image Synthesis - Density Estimation and Photon Mapping - Philipp Slusallek Karol Myszkowski Gurprit Singh Realistic Image Synthesis SS19 Density Estimation and Photon Mapping Karol Myszkowski Overview Today Density

1.36k views • 107 slides
Image formation How are objects in the world captured in Image formation an image? Matlab

Image formation How are objects in the world captured in Image formation an image? Matlab

Image formation How are objects in the world captured in Image formation an image? Matlab tutorial Tuesday, Sept 2 Image formation Physical parameters of image formation object film Geometric Type of projection Camera

190 views • 8 slides
lecture 24 Why learn about photography in this course? image capture - Many computer graphics

lecture 24 Why learn about photography in this course? image capture - Many computer graphics

Geri's Game: Note the background is blurred. lecture 24 Why learn about photography in this course? image capture - Many computer graphics methods use existing photographs e.g. texture & environment mapping, image matting. -

157 views • 5 slides
Buddy Bot- The Futures Rover Image Processing GUI Data Mapping and Publishing EF 230: Caleb

Buddy Bot- The Futures Rover Image Processing GUI Data Mapping and Publishing EF 230: Caleb

Buddy Bot- The Futures Rover Image Processing GUI Data Mapping and Publishing EF 230: Caleb Weatherly, Tag Stork, Cale Overstreet Marketing Video Marketing Analysis Problem: Current Mars rovers have a wide range of capabilities,

67 views • 5 slides
Image Features Sanja Fidler CSC420: Intro to Image Understanding 1 / 64 Image Features Image

Image Features Sanja Fidler CSC420: Intro to Image Understanding 1 / 64 Image Features Image

Image Features Sanja Fidler CSC420: Intro to Image Understanding 1 / 64 Image Features Image features are useful descriptions of local or global image properties designed to accomplish a certain task You may want to choose different features

1.09k views • 71 slides
Technological development and use across disciplines Image: ESA/ATG Medialab Image: CAGE, UiT

Technological development and use across disciplines Image: ESA/ATG Medialab Image: CAGE, UiT

Technological development and use across disciplines Image: ESA/ATG Medialab Image: CAGE, UiT Image: NTNU Applied underwater robotics Lab RV Kronprins Haakon Image/data: nuka.uib.no Selected Norwegian marine observations in the Arctic

272 views • 3 slides
Chapter 4 IMAGE PROC IMAGE PROCESSIN ESSING ILWIS for Windows contains a set of image

Chapter 4 IMAGE PROC IMAGE PROCESSIN ESSING ILWIS for Windows contains a set of image

Chapter 4 IMAGE PROC IMAGE PROCESSIN ESSING ILWIS for Windows contains a set of image processing tools for enhancement and analysis of data from space borne or airborne platforms. In this chapter, the routine applications such as image

1.22k views • 88 slides
Methodology for mapping image processing algorithms on massively parallel processors An NVIDIA

Methodology for mapping image processing algorithms on massively parallel processors An NVIDIA

Methodology for mapping image processing algorithms on massively parallel processors An NVIDIA GPU specific approach Florian Gouin Corinne Ancourt firstname.name@mines-paristech.fr MINES ParisTech PSL Research University, Paris Centre de

554 views • 43 slides
Image Resizing / Goal: Mimic a be7er photographer by Retarge.ng improving image quality

Image Resizing / Goal: Mimic a be7er photographer by Retarge.ng improving image quality

10/4/16 Advanced Image Edi.ng Tools Image Resizing / Goal: Mimic a be7er photographer by Retarge.ng improving image quality Image retarge.ng (resize image) Image comple.on (erase and fill unwanted areas) Texture synthesis

340 views • 10 slides
IM IMAGE-TO TO-IM IMAGE T TRANSLATIO ION W WIT ITH C CONDIT ITIO IONAL AD ADVERSAR

IM IMAGE-TO TO-IM IMAGE T TRANSLATIO ION W WIT ITH C CONDIT ITIO IONAL AD ADVERSAR

IM IMAGE-TO TO-IM IMAGE T TRANSLATIO ION W WIT ITH C CONDIT ITIO IONAL AD ADVERSAR ARIAL AL NETWORKS Yuanjie Lu 9 th April What is image to image? Seems like a language concept can be translated into Chinese, French, Italian,

462 views • 17 slides
Is Digital Technology Image 1 Restructuring the brain? Globally connected Image 2 Image 3 How

Is Digital Technology Image 1 Restructuring the brain? Globally connected Image 2 Image 3 How

Is Digital Technology Image 1 Restructuring the brain? Globally connected Image 2 Image 3 How do we interact? Image 4 Image 5 What do others think? Young people are born into technology, and theyre used to using it 24/7, their brains

614 views • 15 slides
Image Processing Todays Class Image Representations: Matrices Image Representations: RGB,

Image Processing Todays Class Image Representations: Matrices Image Representations: RGB,

CS6501: Deep Learning for Visual Recognition Image Processing Todays Class Image Representations: Matrices Image Representations: RGB, HSV, etc Image Processing: Brightness Image Filtering: Mean Filter, Median Filter Image Filtering:

959 views • 77 slides

More recommend