Image segmentation using scale-space random walks

Authors:
Richard Rzeszutek;Thomas El-Maraghi;Dimitrios Androutsos
Affiliations:
Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario;Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario;Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

Citing 6
Cited 0

Scale-space filtering

Readings in computer vision: issues, problems, principles, and paradigms
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive digital photomontage

ACM SIGGRAPH 2004 Papers
Lazy snapping

ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts

ACM SIGGRAPH 2004 Papers
Random Walks for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many methods for supervised image segmentation exist. One such algorithm, Random Walks, is very fast and accurate when compared to other methods. A drawback to Random Walks is that it has difficulty producing accurate and clean segmentations in the presence of noise. Therefore. we propose an extension to Random Walks that improves its performance without significantly modifying the original algorithm. Our extension, known as "Scale-Space Random Walks", or SSRW, addresses these problems. The SSRW is able to produce more accurate segmentations in the presence of noise while still retaining all of the properties of the original algorithm.