1 / 1

SIEMENS

Unitialized, Globally Optimal, Graph-Based Rectilinear Shape Segmentation - The Opposing Metrics Method. Input. 1st. 2nd. 3rd. 4th. Computer Science Department – Carnegie Mellon University, Pittsburgh Department of Imaging and Visualization – Siemens Corporate Research, Princeton.

briar
Download Presentation

SIEMENS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Unitialized, Globally Optimal, Graph-Based Rectilinear Shape Segmentation - The Opposing Metrics Method Input 1st 2nd 3rd 4th 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 Per2 (P) = 13.9 Per1 (P) = 16 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

More Related