PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Geodesic Star Convexity for Interactive Image Segmentation
Varun Gulshan, Carsten Rother, Antonio Criminisi, Andrew Blake and Andrew Zisserman
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June 2010, San Francisco, CA, USA.

Abstract

In this paper we introduce a new shape constraint for interactive image segmentation. It is an extension of Veksler's star-convexity prior, in two ways: from a single star to multiple stars and from Euclidean rays to Geodesic paths. Global minima of the energy function are obtained subject to these new constraints. We also introduce Geodesic Forests, which exploit the structure of shortest paths in implementing the extended constraints. The star-convexity prior is used here in an interactive setting and this is demonstrated in a practical system. The system is evaluated by means of a robot user to measure the amount of interaction required in a precise way. We also introduce a new and harder dataset which augments the existing Grabcut dataset with images and ground truth taken from the PASCAL VOC segmentation challenge.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:6962
Deposited By:Karteek Alahari
Deposited On:25 June 2010