Segmentation Using Max Flow/Min Cut Graph Cuts Based on "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision.“

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Presentation transcript:

Segmentation Using Max Flow/Min Cut Graph Cuts Based on "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision.“ by Yuri Boykov and Vladimir Kolmogorov.

Graph Cuts in Theory Treat pixels as set of nodes Treat labels (bone/not bone) as source/sink Assign costs for cuts Find most efficient cut Boykov and Kolmogorov

Preliminary Results Import images using Corona imaging package Assign weights to edges based on pixel intensity differences Assign weights to edges based on intensity

Work ahead Image size detection Cost function refinement Display and export functions Run on actual images

Project Timeline Weeks 1-2 – Revive/write code – Test with phantom images Mid-Project Presentation – Preliminary Results Weeks 3-4 – Write display code – Run algorithm on real data – Write report/presentation