Markov Random Fields in Image Segmentation

Kató, Zoltán

doi:10.1561/9781601985897

Cited by 23 publications

(53 citation statements)

References 174 publications

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“…The main family of probabilistic graphical models that have been extensively applied to HSI classification is given by Markov random fields (MRF), which provide powerful and flexible spatial-contextual models for the prior distribution in Bayesian image analysis [53][54][55]. They have been recently used for HSI classification in conjunction with SVM [3,[56][57][58], active learning [59], multinomial logistic regression (MLR) [56,60], subspace projections [57], hierarchical statistical region merging [61], blind source separation and meanfield approximations [62], multidimensional wavelets [63], sparse modeling and Dirichlet distributions [64], and ensemble classifiers [65,66].…”

Section: J Some Notes On Computational Timementioning

confidence: 99%

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

Ghamisi

Maggiori

et al. 2018

IEEE Geosci. Remote Sens. Mag.

284

111

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This is a preprint, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore. Airborne and spaceborne hyperspectral imaging systems have advanced in recent years in terms of spectral and spatial resolution, which makes data sets produced by them a valuable source for land-cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image classification techniques by taking advantage of both spectral and spatial information in a single classification framework. The ECHO (Extraction and Classification of Homogeneous Objects) classifier, which was proposed in 1976, might be the first spectral-spatial classification approach of its kind in the remote sensing community. Since then and especially in the latest years, increasing attention has been dedicated to developing sophisticated spectral-spatial classification methods. There is now a rich literature on this particular topic in the remote sensing community, composing of several fast-growing branches. In this paper, the latest advances in spectral-spatial classification of hyperspectral data are critically reviewed. More than 25 approaches based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning are addressed with an emphasis on discussing their methodological foundations. Examples of experimental results on three benchmark hyperspectral data sets, including both wellknown long-used data and a recent data set resulting from an international contest, are also presented. Moreover, the utilized training and test sets for the aforementioned data sets as well The work of Pedram Ghamisi is supported by the "High Potential Program" of Helmholtz-Zentrum Dresden-Rossendorf.

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Section: J Some Notes On Computational Timementioning

confidence: 99%

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

Ghamisi

Maggiori

et al. 2018

IEEE Geosci. Remote Sens. Mag.

284

111

View full text Add to dashboard Cite

show abstract

“…In image segmentation, where the problem is reduced to assigning labels to pixels, label dependencies are modeled by MRF and an optimal labeling is determined by Bayesian estimation. The main advantage of MRF models is that prior information can be imposed locally through clique potentials [23].…”

Section: Markov Random Field Modelmentioning

confidence: 99%

Unsupervised Segmentation of Multilook Polarimetric Synthetic Aperture Radar Images

Bouhlel

Méric

2019

IEEE Trans. Geosci. Remote Sensing

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This paper proposes a new unsupervised image segmentation method for multilook polarimetric synthetic aperture radar (PolSAR) data. The statistical model for the PolSAR data is considered as a finite mixture of non-Gaussian compound distributions considered as the product of two statistically independent random variates, speckle, and texture. With different texture distributions, the product model leads to various expressions of the compound distribution. The method uses a Markov random field (MRF) model for pixel class labels. The expectation-maximization/maximization of the posterior marginals (EM/MPM) algorithm is used for the simultaneous estimation of texture and speckle parameters and for the segmentation of multilook PolSAR images. Simulated and real PolSAR data are shown to demonstrate the method.

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“…Litjens et al [18] wrote an survey for Deep learning in medical applications which is a mark of the popularity of Deep learning for segmentation. Other technics are also availables: genetic algorithm [10], knowledge stored in ontologies [26] or Random Markov Fields [15]. Deep learning (like Convolutional Neural Network (CNN)) is one of the most efficient and promising tools, and it is widely used.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Kidneys Deformed by Nephroblastoma Using Case-Based Reasoning

Florent

Corbat

Delavelle

et al. 2018

Case-Based Reasoning Research and Development

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Image segmentation is a hot topic in image processing research. Most of the time, segmentation is not fully automated, and a user is required to guide the process in order to obtain correct results. Yet, even with programs, it is a time consuming process. In a medical context, segmentation can provide a lot of information to surgeons, but since this task is manual, it is rarely executed because of time,. Artificial Intelligence (AI) is a powerful approach to create viable solutions for a fully automated treatments. In this paper, we defined a Case-Based Reasoning (CBR) which can enhance region-growing segmentation of kidneys deformed by nephroblastoma. The main problem with region-growing methods is a user needs to place the seeds in the image manually. Automated methods exist but are not efficient every time and often give an over-segmentation. That is why we have designed an adaptation phase which can modify the cordinates of seeds recovered during the retrievial phase. We confronted our CBR approach with manual region growing and Convolutional Neural Network (CNN) to segment kidneys and tumors of CT-scans. Our CBR system succeded in performing the best segmentation for the kidney. keywords: Case-Based Reasoning Convolution Neural Network segmentation cancer tumour healthcare imaging artificial intelligence

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Markov Random Fields in Image Segmentation

Cited by 23 publications

References 174 publications

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

Unsupervised Segmentation of Multilook Polarimetric Synthetic Aperture Radar Images

Segmentation of Kidneys Deformed by Nephroblastoma Using Case-Based Reasoning

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