Image Quilting for Texture Synthesis and Transfer Alexei A. Efros1,2 William T. Freeman2.

Slides:



Advertisements
Similar presentations
Filling Algorithms Pixelwise MRFsChaos Mosaics Patch segments are pasted, overlapping, across the image. Then either: Ambiguities are removed by smoothing.
Advertisements

Texture Symmetry A lecture by Alexey Burshtein. Definitions Regular texture is a periodic pattern containing translation symmetry and (possibly) rotation,
Image Quilting and Apples
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004 Fast and High Quality Overlap Repair for Patch-Based Texture Synthesis.
A Robust Super Resolution Method for Images of 3D Scenes Pablo L. Sala Department of Computer Science University of Toronto.
Data-driven methods: Texture (Sz 10.5) Cs129 Computational Photography James Hays, Brown, Spring 2011 Many slides from Alexei Efros.
November 12, 2013Computer Vision Lecture 12: Texture 1Signature Another popular method of representing shape is called the signature. In order to compute.
Texture Synthesis on [Arbitrary Manifold] Surfaces Presented by: Sam Z. Glassenberg* * Several slides borrowed from Wei/Levoy presentation.
A Novel Approach of Assisting the Visually Impaired to Navigate Path and Avoiding Obstacle-Collisions.
Consistent Spherical Parameterization Arul Asirvatham, Emil Praun (University of Utah) Hugues Hoppe (Microsoft Research)
More details on presentations Aim to speak for ~50 min (after 15 min review, leaving 10 min for discussions) Try to plan discussion topics It’s fine to.
São Paulo Advanced School of Computing (SP-ASC’10). São Paulo, Brazil, July 12-17, 2010 Looking at People Using Partial Least Squares William Robson Schwartz.
Lapped Textures Emil Praun and Adam Finkelstien (Princeton University) Huges Hoppe (Microsoft Research) SIGGRAPH 2000 Presented by Anteneh.
High-Quality Simplification with Generalized Pair Contractions Pavel Borodin,* Stefan Gumhold, # Michael Guthe,* Reinhard Klein* *University of Bonn, Germany.
Layered Texture Matting 葉展岱 范智勝. Outline  Introduction  Implementation Overview  Current Work  Reference.
Improved Image Quilting Jeremy Long David Mould. Introduction   Goal: improve “ minimum error boundary cut ”
Maryia Kazakevich “Texture Synthesis by Patch-Based Sampling” Texture Synthesis by Patch-Based Sampling Real-Time Texture Synthesis By Patch-Based Sampling,
Announcements Project 4 questions? Guest lectures Thursday: Richard Ladner “tactile graphics” Next Tuesday: Jenny Yuen and Jeff Bigham.
Image Quilting for Texture Synthesis & Transfer Alexei Efros (UC Berkeley) Bill Freeman (MERL) +=
Lapped Textures SIGGRAPH 2000 Emil Praun Adam Finkelstein Hugues Hoppe.
Multiple View Geometry : Computational Photography Alexei Efros, CMU, Fall 2005 © Martin Quinn …with a lot of slides stolen from Steve Seitz and.
Image Pyramids and Blending
Stereo & Iterative Graph-Cuts Alex Rav-Acha Vision Course Hebrew University.
3D from multiple views : Rendering and Image Processing Alexei Efros …with a lot of slides stolen from Steve Seitz and Jianbo Shi.
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Near-Regular Texture Analysis and Manipulation Written by: Yanxi Liu Yanxi Liu Wen-Chieh Lin Wen-Chieh Lin James Hays James Hays Presented by: Alex Hadas.
Stereo Computation using Iterative Graph-Cuts
Automatic Photo Pop-up Derek Hoiem Alexei A. Efros Martial Hebert.
Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2006 with a lot of slides stolen from Steve Seitz and.
Part Two Multiresolution Analysis of Arbitrary Meshes M. Eck, T. DeRose, T. Duchamp, H. Hoppe, M. Lounsbery, W. Stuetzle SIGGRAPH 95.
Graphcut Texture: Image and Video Synthesis Using Graph Cuts
Overview of Back Propagation Algorithm
Multiple View Geometry : Computational Photography Alexei Efros, CMU, Fall 2006 © Martin Quinn …with a lot of slides stolen from Steve Seitz and.
Image Pyramids and Blending
1/20 Obtaining Shape from Scanning Electron Microscope Using Hopfield Neural Network Yuji Iwahori 1, Haruki Kawanaka 1, Shinji Fukui 2 and Kenji Funahashi.
S EGMENTATION FOR H ANDWRITTEN D OCUMENTS Omar Alaql Fab. 20, 2014.
Terrain Synthesis by Digital Elevation Models Howard Zhou, Jie Sun, Greg Turk, and James M. Rehg
Plug-in and tutorial development for GIMP- Cathy Irwin, 2004 The Development of Image Completion and Tutorial Plug-ins for the GIMP By: Cathy Irwin Supervisors:
Image Processing Edge detection Filtering: Noise suppresion.
CS 4487/6587 Algorithms for Image Analysis
Data Extraction using Image Similarity CIS 601 Image Processing Ajay Kumar Yadav.
Image Quilting for Texture Synthesis and Transfer Alexei A. Efros (UC Berkeley) William T. Freeman (MERL) Siggraph01 ’
Computer Vision, Robert Pless
December 9, 2014Computer Vision Lecture 23: Motion Analysis 1 Now we will talk about… Motion Analysis.
Advanced Computer Graphics (Fall 2010) CS 283, Lecture 26 Texture Synthesis Ravi Ramamoorthi Slides, lecture.
Scene Completion Using Millions of Photographs James Hays, Alexei A. Efros Carnegie Mellon University ACM SIGGRAPH 2007.
2D Texture Synthesis Instructor: Yizhou Yu. Texture synthesis Goal: increase texture resolution yet keep local texture variation.
Graphcut Textures Image and Video Synthesis Using Graph Cuts
Two Patch-based Algorithms for By-example Texture Synthesis Bruno Galerne MAP5, Université Paris Descartes 1 Master 2 Traitement.
Yizhou Yu Texture-Mapping Real Scenes from Photographs Yizhou Yu Computer Science Division University of California at Berkeley Yizhou Yu Computer Science.
Texture Synthesis by Image Quilting CS766 Class Project Fall 2004 Eric Robinson.
Data-driven Architectural texture mapping Texture mapping Un-textured 3D sceneTextured output Textured Architectures 由于建筑物的3D model和 textures均属于structured.
Image Quality Measures Omar Javed, Sohaib Khan Dr. Mubarak Shah.
DETECTION OF COPY MOVE FORGERY IN DIGITAL IMAGES.
Edge Preserving Spatially Varying Mixtures for Image Segmentation Giorgos Sfikas, Christophoros Nikou, Nikolaos Galatsanos (CVPR 2008) Presented by Lihan.
Hebrew University Image Processing Exercise Class 8 Panoramas – Stitching and Blending Min-Cut Stitching Many slides from Alexei Efros.
Two Patch-based Algorithms for By-example Texture Synthesis
Graphcut Textures:Image and Video Synthesis Using Graph Cuts
Announcements Project 4 out today help session at the end of class.
CS 4501: Introduction to Computer Vision Sparse Feature Detectors: Harris Corner, Difference of Gaussian Connelly Barnes Slides from Jason Lawrence, Fei.
DIGITAL SIGNAL PROCESSING
Range Imaging Through Triangulation
Data-driven methods: Texture 2 (Sz 10.5)
Texture Quality Extensions to Image Quilting
Accuracy of the internal multiple prediction when the angle constraints method is applied to the ISS internal multiple attenuation algorithm. Hichem Ayadi.
Fast Pattern Simulation Using Multi‐Scale Search
Image Quilting for Texture Synthesis & Transfer
Announcements Guest lecture next Tuesday
Texture Synthesis and Transfer
Multi-Information Based GCPs Selection Method
Presentation transcript:

Image Quilting for Texture Synthesis and Transfer Alexei A. Efros1,2 William T. Freeman2

Outline Introduction Introduction Quilting Quilting Texture Transfer Texture Transfer

Introduction Image quilting Image quilting Motivation : one pixel at a time synthesis is most Motivation : one pixel at a time synthesis is most complex complex

Quilting Bi : a square blocks Bi : a square blocks SB : all such overlapping blocks in the input texture SB : all such overlapping blocks in the input texture First step First step simply tile it with blocks taken randomly from SB simply tile it with blocks taken randomly from SB

Second step Second step introduce some overlap in the placement of blocks onto the new image introduce some overlap in the placement of blocks onto the new image search SB for such a block that by some measure agrees with its neighbors along the region of overlap. search SB for such a block that by some measure agrees with its neighbors along the region of overlap.

Finally step Finally step blocks have ragged edges which will allow them to better approximate the features in the texture. blocks have ragged edges which will allow them to better approximate the features in the texture. find a minimum cost path through that error surface and declare that to be the boundary of the new block find a minimum cost path through that error surface and declare that to be the boundary of the new block.

Minimum Error Boundary Cut Minimum Error Boundary Cut error surface is defined as e = error surface is defined as e = compute the cumulative minimum error E for all paths compute the cumulative minimum error E for all paths trace back and find the path of the best cut trace back and find the path of the best cut both a vertical and a horizontal overlap, the minimal paths meet in the middle and the overall minimum is chosen for the cut. both a vertical and a horizontal overlap, the minimal paths meet in the middle and the overall minimum is chosen for the cut.

Algorithm Algorithm

Synthesis Results Synthesis Results

Texture Transfer correspondence map is a spatial map of corresponding quantity over both the texture source image and a controlling target image. correspondence map is a spatial map of corresponding quantity over both the texture source image and a controlling target image. quantity could include image quantity could include image intensity, blurred image intensity intensity, blurred image intensity, local image orientation angles, local image orientation angles

END THANKS EVERYONE