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ICPR
2010
IEEE
15 years 23 days ago
3D Vertebrae Segmentation in CT Images with Random Noises
Exposure levels (X-ray tube amperage and peak kilovoltage) are associated with various noise levels and radiation dose. When higher exposure levels are applied, the images have hi...
Melih Seref Aslan
171
Voted
ICMCS
2009
IEEE
415views Multimedia» more  ICMCS 2009»
14 years 11 months ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel label...
Xiaofeng Wang, Xiao-Ping Zhang
141
Voted
ICIP
2009
IEEE
14 years 11 months ago
Random swap EM algorithm for finite mixture models in image segmentation
The Expectation-Maximization (EM) algorithm is a popular tool in statistical estimation problems involving incomplete data or in problems which can be posed in a similar form, suc...
Qinpei Zhao, Ville Hautamäki, Ismo Kärkk...
ICCV
2009
IEEE
1714views Computer Vision» more  ICCV 2009»
16 years 6 months ago
Power watersheds: a new image segmentation framework extending graph cuts, random walker and optimal spanning forest
In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, ran- dom walker, and shortest path optimization algorithms. Viewing an ima...
Camille Couprie, Leo Grady, Laurent Najman, Hugues...
PAMI
2010
396views more  PAMI 2010»
15 years 5 days ago
Self-Validated Labeling of Markov Random Fields for Image Segmentation
—This paper addresses the problem of self-validated labeling of Markov random fields (MRFs), namely to optimize an MRF with unknown number of labels. We present graduated graph c...
Wei Feng, Jiaya Jia, Zhi-Qiang Liu