Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Edge-Labeling Using Dictionary-Based Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring Surface Trace and Differential Structure from 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to track the visual motion of contours
Artificial Intelligence - Special volume on computer vision
Iterative curve organisation with the EM algorithm
Pattern Recognition Letters
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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This paper describes a novel approach to surface tracking in volumetric image stacks. It draws on a statistical model of the uncertainties inherent in the characterisation of feature contours to compute an evidential field for putative inter-frame displacements. This field is computed using Gaussian density kernels which are parameterised in terms of the variance-covariance matrices for contour displacement. The underlying variance model accommodates the effects of raw image noise on the estimated surface normals. The evidential field effectively couples contour displacements to the intensity features on successive frames through a statistical process of contour tracking. Hard contours are extracted using a dictionary-based relaxation process. The method is evaluated on both MRI data and simulated data.