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Highly Cited

2009

Highly Cited

2009

By reading, you can know the knowledge and things more, not only about what you get from people to people. Book will be more… Expand

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Highly Cited

2005

Highly Cited

2005

This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for… Expand

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Highly Cited

2005

Highly Cited

2005

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics, a very active area of research in which… Expand

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Highly Cited

2005

Highly Cited

2005

This paper describes a highly successful application of MRFs to the problem of generating high-resolution range images. A new… Expand

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Highly Cited

2003

Highly Cited

2003

We introduce a method to solve exactly a first order Markov random field optimization problem in more generality than was… Expand

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Highly Cited

2001

Highly Cited

2001

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It… Expand

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Highly Cited

1998

Highly Cited

1998

Markov Random Fields (MRFs) can be used for a wide variety of vision problems. In this paper we focus on MRFs with two-valued… Expand

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Highly Cited

1996

Highly Cited

1996

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A… Expand

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Highly Cited

1995

Highly Cited

1995

From the Publisher:
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing… Expand

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Highly Cited

1985

Highly Cited

1985

The problem of texture classification arises in several disciplines such as remote sensing, computer vision, and image analysis… Expand

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