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» Discrete Mixture Models for Unsupervised Image Segmentation
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VLSM
2005
Springer
14 years 1 months ago
Advances in Variational Image Segmentation Using AM-FM Models: Regularized Demodulation and Probabilistic Cue Integration
Current state-of-the-art methods in variational image segmentation using level set methods are able to robustly segment complex textured images in an unsupervised manner. In recent...
Georgios Evangelopoulos, Iasonas Kokkinos, Petros ...
CRV
2009
IEEE
237views Robotics» more  CRV 2009»
14 years 2 months ago
SEC: Stochastic Ensemble Consensus Approach to Unsupervised SAR Sea-Ice Segmentation
The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment ...
Alexander Wong, David A. Clausi, Paul W. Fieguth
ISBI
2004
IEEE
14 years 8 months ago
A Probabilistic Framework for the Detection and Tracking in Time of Multiple Sclerosis Lesions
A novel statistical scheme for the automatic detection and tracking in time of relapsing-remitting multiple sclerosis (MS) lesions in image sequences is described. Coherent space-...
Allon Shahar, Hayit Greenspan
SDM
2004
SIAM
218views Data Mining» more  SDM 2004»
13 years 9 months ago
Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Ashok N. Srivastava