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PAMI
2010
396views more  PAMI 2010»
13 years 7 months 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
CVPR
2004
IEEE
14 years 10 months ago
Globally Optimal Segmentation of Interacting Surfaces with Geometric Constraints
Abstract-- Efficient detection of globally optimal surfaces representing object boundaries in volumetric datasets is important and remains challenging in many medical image analysi...
Kang Li, Xiaodong Wu, Danny Z. Chen, Milan Sonka
ICVGIP
2004
13 years 10 months ago
A Robust Nonparametric Estimation Framework for Implicit Image Models
Robust model fitting is important for computer vision tasks due to the occurrence of multiple model instances, and, unknown nature of noise. The linear errors-in-variables (EIV) m...
Himanshu Arora, Maneesh Singh, Narendra Ahuja
CVPR
2007
IEEE
14 years 10 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen
ECCV
2010
Springer
13 years 9 months ago
Superpixels and Supervoxels in an Energy Optimization Framework
Many methods for object recognition, segmentation, etc., rely on tessellation of an image into "superpixels". A superpixel is an image patch which is better aligned with ...
Olga Veksler, Yuri Boykov, Paria Mehrani