Robust Dynamical Model for Simultaneous Registration and Segmentation in a Variational Framework: A Bayesian Approach

Pratim Ghosh, Mehmet Emre Sargin, and B.S. Manjunath,
Department of Electrical and Computer Engineering,
University of California, Santa Barbara.

pratim [at] ece.ucsb.edu

Abstract

We introduce a dynamical model for simultaneous registration and segmentation in a variational framework for image sequences, where the dynamics is incorporated using a Bayesian formulation. A linear stochastic equation relating the tracked object (or a region of interest) is first derived under the assumption that the successive images in the sequence are related by a dense and possibly non-linear displacement field. This derivation allows for the use of a computationally efficient and recursive implementation of the Bayesian formulation in this framework. The contour of the tracked object returned by the dynamical model is not only close to the previously detected shape but is also consistent with the temporal statistics of the tracked object. The performance of the proposed approach is evaluated on real image sequences. It is shown that, with respect to a variety of error metrics such as F-measure, mean absolute deviation and Hausdorff distance, the proposed approach outperforms the state-of-the art approach without the dynamical model.
[PDF] [BibTex]
Pratim Ghosh, Mehmet Emre Sargin, and B.S. Manjunath,
12th IEEE International Conference on Computer Vision, Kyoto, Japan, Oct. 2009.
Node ID: 532 , DB ID: 339 , Lab: VRL , Target: Conference
Subject: [Image and Video Segmentation] « Look up more