Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Novel Medical Image Segmentation Method using Dynamic Programming
MEDIVIS '07 Proceedings of the International Conference on Medical Information Visualisation - BioMedical Visualisation
Contour grouping with prior models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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With an increase in the percentage of people over the age of 65 years or older, there is a growing interest in finding ways to slow or reverse some of the effects of aging on the human body. One of these effects is the loss in the ability of the human eye to visually focus on near objects. Understanding the properties of the natural lens and the associated mechanisms of accommodation will greatly increase our knowledge to identify alternate solutions to reverse this phenomenon. Towards that end, a technique called photorefraction is currently being used in laboratory studies involving monkeys. To quantitate the lens properties, there is a need to accurately measure monkey lenses in images produced by this technique. In this paper, we present two probabilistic methods for segmenting the lens from photorefraction video sequences. Results of the developed methods are compared and evaluated against ground-truth segmentations. In addition, the results obtained are also compared to those obtained by a level set segmentation method.