Concept

Couverture de Markov

Publications associées (22)

Neural conditional random fields

We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we ap ...

JMLR: W&CP2010

Channel estimation by inference on Gaussian Markov random fields

Thomas Riedl

In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps ...

2009

Contextual classification of image patches with latent aspect models

Daniel Gatica-Perez, Jean-Marc Odobez, Florent Monay Michaud, Pedro Manuel Da Silva Quelhas

We present a novel approach for contextual classification of image patches in complex visual scenes, based on the use of histograms of quantized features and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that spe ...

2009

Spin Glass Models of Markov Random Fields

Barbara Caputo

This paper presents a novel algorithm for robust object recognition. We propose to model the visual appearance of objects via probability density functions. The algorithm consists of a fully connected Markov random field with energy function derived from r ...

2006

Point Similarity Measures Based on MRF Modeling of Difference Images for Spline-Based 2D-3D Rigid Registration of x-Ray Fluoroscopy to CT Images

Michaël Unser, Philippe Thévenaz, Xiuwei Zhang

One of the main factors that affect the accuracy of intensity-based registration of two-dimensional (2D) X-ray fluoroscopy to three-dimensional (3D) CT data is the similarity measure, which is a criterion function that is used in the registration procedure ...

Springer2006

Text detection and recognition in images and video sequences

Datong Chen

Text characters embedded in images and video sequences represents a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, gra ...

2003

Text detection and recognition in images and video sequences

Datong Chen

École Polytechnique Fédérale de Lausanne2003

Text detection and recognition in images and video sequences

Datong Chen

EPFL2003

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Hervé Bourlard, Jean-Marc Odobez, Datong Chen

In this paper we propose a method to segment and recognize text embedded in video and images. We modelize the gray level distribution in the text images as mixture of gaussians, and then assign each pixel to one of the gaussian layer. The assignment is bas ...

2002

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Hervé Bourlard, Jean-Marc Odobez, Datong Chen

IDIAP2002

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