Unitialized, Globally Optimal, Graph-Based Rectilinear Shape Segmentation - The Opposing Metrics Method Computer Science Department – Carnegie Mellon University,

Slides:

Advertisements

Similar presentations

Semi-Supervised Learning in Gigantic Image Collections

Advertisements

Variable Metric For Binary Vector Quantization UNIVERSITY OF JOENSUU DEPARTMENT OF COMPUTER SCIENCE JOENSUU, FINLAND Ismo Kärkkäinen and Pasi Fränti.

Ignas Budvytis*, Tae-Kyun Kim*, Roberto Cipolla * - indicates equal contribution Making a Shallow Network Deep: Growing a Tree from Decision Regions of.

1 Perceptual Organization and Linear Algebra Charless Fowlkes Computer Science Dept. University of California at Berkeley.

Semi-supervised Learning Rong Jin. Semi-supervised learning  Label propagation  Transductive learning  Co-training  Active learning.

S I E M E N S C O R P O R A T E R E S E A R C H 1 1 General Purpose Image Segmentation with Random Walks Leo Grady Department of Imaging and Visualization.

Normalized Cuts and Image Segmentation

Online Social Networks and Media. Graph partitioning The general problem – Input: a graph G=(V,E) edge (u,v) denotes similarity between u and v weighted.

Clustering II CMPUT 466/551 Nilanjan Ray. Mean-shift Clustering Will show slides from:

1School of CS&Eng The Hebrew University

S I E M E N S C O R P O R A T E R E S E A R C H 1 1 A Seeded Image Segmentation Framework Unifying Graph Cuts and Random Walker Which Yields A New Algorithm.

Image Segmentation some examples Zhiqiang wang

Corp. Research Princeton, NJ Computing geodesics and minimal surfaces via graph cuts Yuri Boykov, Siemens Research, Princeton, NJ joint work with Vladimir.

Object Detection by Matching Longin Jan Latecki. Contour-based object detection Database shapes: …..

10/11/2001Random walks and spectral segmentation1 CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View.

Silhouettes in Multiview Stereo Ian Simon. Multiview Stereo Problem Input: – a collection of images of a rigid object (or scene) – camera parameters for.

Data Structures For Image Analysis

A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts Dhillon, Inderjit S., Yuqiang Guan, and Brian Kulis.

Normalized Cuts and Image Segmentation Jianbo Shi and Jitendra Malik, Presented by: Alireza Tavakkoli.

Image Segmentation Chapter 14, David A. Forsyth and Jean Ponce, “Computer Vision: A Modern Approach”.

A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts

A Closed Form Solution to Natural Image Matting

EDA (CS286.5b) Day 6 Partitioning: Spectral + MinCut.

Semi-Supervised Learning in Gigantic Image Collections Rob Fergus (NYU) Yair Weiss (Hebrew U.) Antonio Torralba (MIT) TexPoint fonts used in EMF. Read.

MRF Labeling With Graph Cut CMPUT 615 Nilanjan Ray.

Lecture#6: segmentation Anat Levin Introduction to Computer Vision Class Fall 2009 Department of Computer Science and App math, Weizmann Institute of Science.

Comp 775: Graph Cuts and Continuous Maximal Flows Marc Niethammer, Stephen Pizer Department of Computer Science University of North Carolina, Chapel Hill.

Presentation By Michael Tao and Patrick Virtue. Agenda History of the problem Graph cut background Compute graph cut Extensions State of the art Continued.

Image Segmentation Rob Atlas Nick Bridle Evan Radkoff.

S I E M E N S C O R P O R A T E R E S E A R C H 1 1 Computing Exact Discrete Minimal Surfaces: Extending and Solving the Shortest Path Problem in 3D with.

Manifold learning: Locally Linear Embedding Jieping Ye Department of Computer Science and Engineering Arizona State University

Segmentation using eigenvectors

Segmentation using eigenvectors Papers: “Normalized Cuts and Image Segmentation”. Jianbo Shi and Jitendra Malik, IEEE, 2000 “Segmentation using eigenvectors:

EE 492 ENGINEERING PROJECT LIP TRACKING Yusuf Ziya Işık & Ashat Turlibayev Yusuf Ziya Işık & Ashat Turlibayev Advisor: Prof. Dr. Bülent Sankur Advisor:

Chapter 14: SEGMENTATION BY CLUSTERING 1. 2 Outline Introduction Human Vision & Gestalt Properties Applications – Background Subtraction – Shot Boundary.

Random Walk with Restart (RWR) for Image Segmentation

Relative Volume Constraints for 3D Image Editing Computer Vision Group TU Munich Eno Töppe, Claudia Nieuwenhuis, Daniel Cremers May 25th, 2012.

How to reform a terrain into a pyramid Takeshi Tokuyama (Tohoku U) Joint work with Jinhee Chun (Tohoku U) Naoki Katoh (Kyoto U) Danny Chen (U. Notre Dame)

Graph Cuts Marc Niethammer. Segmentation by Graph-Cuts A way to compute solutions to the optimization problems we looked at before. Example: Binary Segmentation.

CSE554AlignmentSlide 1 CSE 554 Lecture 8: Alignment Fall 2013.

Project by: Cirill Aizenberg, Dima Altshuler Supervisor: Erez Berkovich.

Graph-based Deformable Matching of 3D Line Segments with Application in Protein Fitting 12 1 HANG DOU 1, MATTHEW L BAKER 2, TAO JU Washington University.

COS429 Computer Vision =++ Assignment 4 Cloning Yourself.

About Me Swaroop Butala  MSCS – graduating in Dec 09  Specialization: Systems and Databases  Interests:  Learning new technologies  Application of.

 In the previews parts we have seen some kind of segmentation method.  In this lecture we will see graph cut, which is a another segmentation method.

Mesh Segmentation via Spectral Embedding and Contour Analysis Speaker: Min Meng

Normalized Cuts and Image Segmentation Patrick Denis COSC 6121 York University Jianbo Shi and Jitendra Malik.

Image segmentation.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Dimensionality Reduction

Clustering Usman Roshan.

University of Ioannina

Mean Shift Segmentation

Local Feature Extraction Using Scale-Space Decomposition

Graph Cut Weizhen Jing

ECE 692 – Advanced Topics in Computer Vision

Outline H. Murase, and S. K. Nayar, “Visual learning and recognition of 3-D objects from appearance,” International Journal of Computer Vision, vol. 14,

Mesh Parameterization: Theory and Practice

Lecture 31: Graph-Based Image Segmentation

Introduction PCA (Principal Component Analysis) Characteristics:

Seam Carving Project 1a due at midnight tonight.

Spectral Clustering Eric Xing Lecture 8, August 13, 2010

3.3 Network-Centric Community Detection

EE 492 ENGINEERING PROJECT

Solve the equation: 6 x - 2 = 7 x + 7 Select the correct answer.

Calibration and homographies

“Traditional” image segmentation

Clustering Usman Roshan CS 675.

Presentation transcript:

Unitialized, Globally Optimal, Graph-Based Rectilinear Shape Segmentation - The Opposing Metrics Method Computer Science Department – Carnegie Mellon University, Pittsburgh Department of Imaging and Visualization – Siemens Corporate Research, Princeton Ali Kemal Sinop and Leo Grady SIEMENS asinop@cmu.edu, Leo.Grady@siemens.com Main Idea Weak boundary completion Optimization Globally optimal variational segmentation requires two propreties: 1) Ability to measure “shapeness” of a segmentation 2) Ability to find a segmentation that optimizes the shapeness measure Rectilinearity measure For a given boundary P, measure rectilinearity as the ratio x – binary indicator vector on nodes L1 – Laplacian matrix of L1 graph L2 – Laplacian matrix of L2 graph Kaniza square gen. eigenvector segmentation Relax binary formulation to allow real values for x Generalized eigenvector problem! Merged circle/square gen. eigenvector segmentation Threshold solution x at value producing maximal ratio for an intrinsic parameterization in terms of u and v For a segmentation on lattice, L1 and L2 boundary metrics representable as cuts on weighted graph Natural image results Effect of resolution on measure Per1 (P) = 16 Per2 (P) = 13.9 Q(P) = 1 Graph formulation allows us to measure exact dependence of shape descriptor on the number of pixels comprising object Q(P) = .8536 Per1 (P) = 16 Per2 (P) = 11.8 Correctness Input 1st 2nd 3rd 4th