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» Learning a Classification Model for Segmentation
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ICML
2000
IEEE
14 years 10 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
ICIP
2010
IEEE
13 years 7 months ago
Combining free energy score spaces with information theoretic kernels: Application to scene classification
Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vecto...
Manuele Bicego, Alessandro Perina, Vittorio Murino...
CVPR
2011
IEEE
13 years 1 months ago
A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes
Simultaneously segmenting and labeling images is a fundamental problem in Computer Vision. In this paper, we introduce a hierarchical CRF model to deal with the problem of labelin...
Qixing Huang, Mei Han, Bo Wu, Sergey Ioffe
ICIAR
2010
Springer
13 years 6 months ago
Image Segmentation for Robots: Fast Self-adapting Gaussian Mixture Model
Image segmentation is a critical low-level visual routine for robot perception. However, most image segmentation approaches are still too slow to allow real-time robot operation. I...
Nicola Greggio, Alexandre Bernardino, José ...
ISBI
2008
IEEE
14 years 10 months ago
Segmentation of the evolving left ventricle by learning the dynamics
We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learn...
Walter Sun, Müjdat Çetin, Raymond Chan...