scholarly journals Markov Models for Image Labeling

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
S. Y. Chen ◽  
Hanyang Tong ◽  
Carlo Cattani
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Discrete Optimization ◽  
Belief Propagation ◽  
Markov Models ◽  
Optimization Methods ◽  
Image Labeling ◽  
Markov Random ◽  
Basic Concepts ◽  
Fundamental Formulation

Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.

scholarly journals Conditional Random Fields for Image Labeling

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Tong Liu ◽  
Xiutian Huang ◽  
Jianshe Ma
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Markov Models ◽  
Rapid Development ◽  
Mean Field ◽  
Optimization Methods ◽  
Field Approximation ◽  
Graph Cut ◽  
Mean Field Approximation ◽  
Image Labeling

With the rapid development and application of CRFs (Conditional Random Fields) in computer vision, many researchers have made some outstanding progress in this domain because CRFs solve the classical version of the label bias problem with respect to MEMMs (maximum entropy Markov models) and HMMs (hidden Markov models). This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean field approximation. This paper describes graph cut briefly while it introduces mean field approximation more detailedly which has a substantial speed of inference and is researched popularly in recent years.

scholarly journals Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1516 ◽  
Author(s):  
Francisco Troncoso-Pastoriza ◽  
Pablo Eguía-Oller ◽  
Rebeca Díaz-Redondo ◽  
Enrique Granada-Álvarez ◽  
Aitor Erkoreka
Keyword(s):  
Computer Vision ◽  
Optimization Methods ◽  
Geometric Information ◽  
Constrained System ◽  
Building Information Modelling ◽  
Correct Model ◽  
Reprojection Error ◽  
Localization Errors ◽  
Building Information ◽  
Error Filtering

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.

Color Image Segmentation Using Entropy of Fuzziness and Markov Random Field

1997 ◽  
Vol 30 (7) ◽  
pp. 269-274
Author(s):  
R. Boussarsar ◽  
P. Martin ◽  
R. Lecordier ◽  
M. Ketata
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Color Image ◽  
Color Image Segmentation ◽  
Markov Random

Texture Image Segmentation Based on MRMRF in Contourlet Domain

2012 ◽  
Vol 532-533 ◽  
pp. 732-737
Author(s):  
Xi Jie Wang ◽  
Xiao Fan Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Field Model ◽  
Model Parameters ◽  
Texture Image ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Texture Image Segmentation

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

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