DocumentCode
2712690
Title
Automated reconstruction of tree structures using path classifiers and Mixed Integer Programming
Author
Türetken, Engin ; Benmansour, Fethallah ; Fua, Pascal
Author_Institution
Comput. Vision Lab. (EPFL), Lausanne, Switzerland
fYear
2012
fDate
16-21 June 2012
Firstpage
566
Lastpage
573
Abstract
Although tracing linear structures in 2D images and 3D image stacks has received much attention over the years, full automation remains elusive. In this paper, we formulate the delineation problem as one of solving a Quadratic Mixed Integer Program (Q-MIP) in a graph of potential paths, which can be done optimally up to a very small tolerance. We further propose a novel approach to weighting these paths, which results in a Q-MIP solution that accurately matches the ground truth. We demonstrate that our approach outperforms a state-of-the-art technique based on the k-Minimum Spanning Tree formulation on a 2D dataset of aerial images and a 3D dataset of confocal microscopy stacks.
Keywords
graph theory; image reconstruction; integer programming; tree data structures; 2D images; 3D image stacks; Q-MIP; aerial images; automated reconstruction; confocal microscopy stacks; delineation problem; graph theory; k-minimum spanning tree; linear structures; mixed integer programming; path classifiers; potential paths; quadratic mixed integer program; tree structures; Histograms; Image edge detection; Image reconstruction; Joining processes; Noise; Robustness; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location
Providence, RI
ISSN
1063-6919
Print_ISBN
978-1-4673-1226-4
Electronic_ISBN
1063-6919
Type
conf
DOI
10.1109/CVPR.2012.6247722
Filename
6247722
Link To Document