Slides from Alexei Efros

Slides:

Advertisements

Similar presentations

CSCE 643 Computer Vision: Template Matching, Image Pyramids and Denoising Jinxiang Chai.

Advertisements

Multiscale Analysis of Images Gilad Lerman Math 5467 (stealing slides from Gonzalez & Woods, and Efros)

15-463: Computational Photography Alexei Efros, CMU, Spring 2010 Most slides from Steve Marschner Sampling and Reconstruction.

Ted Adelson’s checkerboard illusion. Motion illusion, rotating snakes.

CS 691 Computational Photography

Computational Photography: Sampling + Reconstruction Connelly Barnes Slides from Alexei Efros and Steve Marschner.

Pyramids and Texture. Scaled representations Big bars and little bars are both interesting Spots and hands vs. stripes and hairs Inefficient to detect.

Computational Photography CSE 590 Tamara Berg Filtering & Pyramids.

HCI/ComS 575X: Computational Perception Instructor: Alexander Stoytchev

Convolution. Spatial Filtering Operations g(x,y) = 1/M  f(n,m) (n,m) in  S Example 3 x 3 5 x 5.

Image Filtering, Part 2: Resampling Today’s readings Forsyth & Ponce, chapters Forsyth & Ponce –

Convolution, Edge Detection, Sampling : Computational Photography Alexei Efros, CMU, Fall 2006 Some slides from Steve Seitz.

CSCE 641 Computer Graphics: Image Sampling and Reconstruction Jinxiang Chai.

Sampling and Pyramids : Rendering and Image Processing Alexei Efros …with lots of slides from Steve Seitz.

Edges and Scale Today’s reading Cipolla & Gee on edge detection (available online)Cipolla & Gee on edge detection Szeliski – From Sandlot ScienceSandlot.

Image transformations, Part 2 Prof. Noah Snavely CS1114

CSCE 641 Computer Graphics: Image Sampling and Reconstruction Jinxiang Chai.

Image Pyramids and Blending

Image Sampling Moire patterns

Lecture 4: Image Resampling CS4670: Computer Vision Noah Snavely.

Texture Reading: Chapter 9 (skip 9.4) Key issue: How do we represent texture? Topics: –Texture segmentation –Texture-based matching –Texture synthesis.

15-463: Computational Photography Alexei Efros, CMU, Fall 2011 Many slides from Steve Marschner Sampling and Reconstruction.

Convolution. Convolution Properties Commutative: f*g = g*f Associative: (f*g)*h = f*(g*h) Homogeneous : f*( g)= f*g Additive (Distributive): f*(g+h)=

Lecture 3: Image Resampling CS4670: Computer Vision Noah Snavely Nearest-neighbor interpolation Input image 3x upsample hq3x interpolation (ZSNES)

Image Pyramids and Blending : Computational Photography Alexei Efros, CMU, Fall 2005 © Kenneth Kwan.

Slides from Alexei Efros, Steve Marschner Filters & fourier theory.

Image Sampling Moire patterns -

Image Pyramids and Blending

Recap from Friday Pinhole camera model Perspective projections Lenses and their flaws Focus Depth of field Focal length and field of view.

Image Sampling CSE 455 Ali Farhadi Many slides from Steve Seitz and Larry Zitnick.

CSC589 Introduction to Computer Vision Lecture 9 Sampling, Gaussian Pyramid Bei Xiao.

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5409 T-R 10:30am – 11:50am.

2D Image Fourier Spectrum.

Applications of Image Filters Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem 02/04/10.

Image Resampling CS4670: Computer Vision Noah Snavely.

Image Resampling CS4670: Computer Vision Noah Snavely.

Image Processing Xuejin Chen Ref:

Lecture 4: Image Resampling and Reconstruction CS4670: Computer Vision Kavita Bala.

Lecture 3: Edge detection CS4670/5670: Computer Vision Kavita Bala From Sandlot ScienceSandlot Science.

Recap from Lecture 2 Pinhole camera model Perspective projections Lenses and their flaws Focus Depth of field Focal length and field of view Chapter 2.

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm.

Brent M. Dingle, Ph.D Game Design and Development Program Mathematics, Statistics and Computer Science University of Wisconsin - Stout Image Processing.

Lecture 5: Fourier and Pyramids

Projects Project 1a due this Friday Project 1b will go out on Friday to be done in pairs start looking for a partner now.

Recall: Gaussian smoothing/sampling G 1/4 G 1/8 Gaussian 1/2 Solution: filter the image, then subsample Filter size should double for each ½ size reduction.

CS 4501: Introduction to Computer Vision Filtering and Edge Detection

Image Resampling & Interpolation

Many slides from Steve Seitz and Larry Zitnick

CS 4501: Introduction to Computer Vision Sparse Feature Detectors: Harris Corner, Difference of Gaussian Connelly Barnes Slides from Jason Lawrence, Fei.

Image Sampling Moire patterns

Gonzalez & Woods, and Efros)

Image Sampling Moire patterns

Jeremy Bolton, PhD Assistant Teaching Professor

Multiscale Analysis of Images

More Image Manipulation

Sampling and Reconstruction

Samuel Cheng Slide credits: Noah Snavely

Image Processing Today’s readings For Monday

Filtering Part 2: Image Sampling

Image Sampling Moire patterns

Oh, no, wait, sorry, I meant, welcome to the implementation of 20th-century science fiction literature, or a small part of it, where we will learn about.

Image Resampling & Interpolation

Samuel Cheng Slide credits: Noah Snavely

Presentation transcript:

Slides from Alexei Efros Gaussian Pyramid Slides from Alexei Efros

Sampling Good sampling: Bad sampling: Sample often or, Sample wisely see aliasing in action!

Gaussian pre-filtering Solution: filter the image, then subsample Filter size should double for each ½ size reduction. Why?

Subsampling with Gaussian pre-filtering Solution: filter the image, then subsample Filter size should double for each ½ size reduction. Why? How can we speed this up?

Compare with... 1/2 1/4 (2x zoom) 1/8 (4x zoom) Why does this look so crufty?

Image Pyramids Known as a Gaussian Pyramid [Burt and Adelson, 1983] In computer graphics, a mip map [Williams, 1983] A precursor to wavelet transform

A bar in the big images is a hair on the zebra’s nose; in smaller images, a stripe; in the smallest, the animal’s nose Figure from David Forsyth

Gaussian pyramid construction filter mask Repeat Filter Subsample Until minimum resolution reached can specify desired number of levels (e.g., 3-level pyramid) The whole pyramid is only 4/3 the size of the original image!

Gaussian pyramid construction is similar to Gaussian

Laplacian Pyramid Laplacian Pyramid decomposition Gaussian Pyramid Created from Gaussian pyramid by subtraction

Laplacian Pyramid Laplacian Pyramid decomposition Gaussian Pyramid Created from Gaussian pyramid by subtraction

What are they good for? Improve Search Precomputation Image Processing Search over translations Like homework Classic coarse-to-fine stategy Search over scale Template matching E.g. find a face at different scales Precomputation Need to access image at different blur levels Useful for texture mapping at different resolutions (called mip-mapping) Image Processing Editing frequency bands separetly E.g. image blending… next time!