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» Learning By Observation Using Qualitative Spatial Relations
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IJCV
2006
161views more  IJCV 2006»
13 years 7 months ago
Discriminative Random Fields
In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and mode...
Sanjiv Kumar, Martial Hebert
NIPS
1998
13 years 9 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
ICML
2000
IEEE
14 years 8 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...
ICDM
2007
IEEE
183views Data Mining» more  ICDM 2007»
14 years 2 months ago
Depth-Based Novelty Detection and Its Application to Taxonomic Research
It is estimated that less than 10 percent of the world’s species have been described, yet species are being lost daily due to human destruction of natural habitats. The job of d...
Yixin Chen, Henry L. Bart Jr., Xin Dang, Hanxiang ...
MICCAI
2005
Springer
14 years 8 months ago
Tissue Classification of Noisy MR Brain Images Using Constrained GMM
We present an automated algorithm for tissue segmentation of noisy, low contrast magnetic resonance (MR) images of the brain. We use a mixture model composed of a large number of G...
Amit Ruf, Hayit Greenspan, Jacob Goldberger