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» Image Distance Using Hidden Markov Models
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MICCAI
2009
Springer
14 years 6 months ago
Fast Automatic Segmentation of the Esophagus from 3D CT Data Using a Probabilistic Model
Automated segmentation of the esophagus in CT images is of high value to radiologists for oncological examinations of the mediastinum. It can serve as a guideline and prevent confu...
Johannes Feulner, Shaohua Kevin Zhou, Alexander Ca...
PAMI
2008
137views more  PAMI 2008»
13 years 9 months ago
IRGS: Image Segmentation Using Edge Penalties and Region Growing
This paper proposes an image segmentation method named iterative region growing using semantics (IRGS), which is characterized by two aspects. First, it uses graduated increased ed...
Qiyao Yu, David A. Clausi
3DPVT
2006
IEEE
188views Visualization» more  3DPVT 2006»
14 years 29 days ago
Statistical Inference of Biological Structure and Point Spread Functions in 3D Microscopy
We present a novel method for detecting and quantifying 3D structure in stacks of microscopic images captured at incremental focal lengths. We express the image data as stochastic...
Joseph Schlecht, Kobus Barnard, Barry Pryor
PAMI
2008
176views more  PAMI 2008»
13 years 9 months ago
Learning Flexible Features for Conditional Random Fields
Abstract-- Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increas...
Liam Stewart, Xuming He, Richard S. Zemel
NIPS
1997
13 years 10 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung