Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing

Lakshmanan, Sridhar; Derin, Haluk

doi:10.1109/34.31443

Cited by 341 publications

(162 citation statements)

References 13 publications

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“…The most widely used general method is the so-called ''Expectation-Maximization'' (EM) method [20]; however, its implementation in the hidden Markov fields context is difficult [6,20,24] and some alternative methods have been proposed [3,9,15,17,24,34,35]. In particular, one may consider Stochastic Gradient (SG [35]), whose aim is to approach the maximum of the likelihood p (y) in a stochastic manner to remedy the difficulties encountered by EM.…”

Section: Parameter Estimationmentioning

confidence: 99%

Unsupervised image segmentation using triplet Markov fields

Benboudjema¹,

Pieczynski²

2005

Computer Vision and Image Understanding

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Hidden Markov fields (HMF) models are widely applied to various problems arising in image processing. In these models, the hidden process of interest X is a Markov field and must be estimated from its observable noisy version Y. The success of HMF is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed one remains Markovian, which facilitates different processing strategies such as Bayesian restoration. HMF have been recently generalized to ''pairwise'' Markov fields (PMF), which offer similar processing advantages and superior modeling capabilities. In PMF one directly assumes the Markovianity of the pair (X, Y). Afterwards, ''triplet'' Markov fields (TMF), in which the distribution of the pair (X, Y) is the marginal distribution of a Markov field (X, U, Y), where U is an auxiliary process, have been proposed and still allow restoration processing. The aim of this paper is to propose a new parameter estimation method adapted to TMF, and to study the corresponding unsupervised image segmentation methods. The latter are validated via experiments and real image processing.

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Section: Parameter Estimationmentioning

confidence: 99%

Unsupervised image segmentation using triplet Markov fields

Benboudjema¹,

Pieczynski²

2005

Computer Vision and Image Understanding

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“…The map was determined manually by sampling tissue types throughout the field to decide the average inhomogeneity. Note that more complicated MR image models ( [7], [11], [10]) can be used to calculate p(O). …”

Section: %4mentioning

confidence: 99%

Segmentation and measurement of the cortex from 3D MR images

Zeng

Staib

Schultz

et al. 1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

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Abstract. The cortex is the outermost thin layer of gray matter in the brain; geometric measurement of the cortex helps in understanding brain anatomy and function. In the quantitative analysis of the cortex from MR images, extracting the structure and obtaining a representation for various measurements are key steps. While manual segmentation is tedious and labor intensive, automatic, reliable and efficient segmentation and measurement of the cortex remain challenging problems due to its convoluted nature. A new approach of coupled surfaces propagation using level set methods is presented here for the problem of the segmentation and measurement of the cortex. Our method is motivated by the nearly constant thickness of the cortical mantle and takes this tight coupling as an important constraint. By evolving two embedded surfaces simultaneously, each driven by its own image-derived information while maintaining the coupling, a final representation of the cortical bounding surfaces and an automatic segmentation of the cortex are achieved. Characteristics of the cortex such as cortical surface area, surface curvature and thickness are then evaluated. The level set implementation of surface propagation offers the advantage of easy initialization, computational efficiency and the ability to capture deep folds of the sulci. Results and validation from various experiments on simulated and real 3D MR images are provided.

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“…In [39], the authors use the Metropolis-Hastings algorithm to estimate the MRF parameters. Lakshmanan and Derin [40] have developed a iterative algorithm for MAP segmentation using an ML estimate of the MRF parameters. Nadabar and Jain [41] estimate the MRF line process parameters using geometric computer-aided design (CAD) models of the objects in the scene.…”

mentioning

confidence: 99%

A Learning-Based Method for Image Super-Resolution From Zoomed Observations

Joshi¹,

Chaudhuri

Panuganti

2005

IEEE Trans. Syst., Man, Cybern. B

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Abstract-We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the most zoomed observation. Assuming a homogeneity of the high-resolution field, the learned model is used as a prior while super-resolving the scene. We suggest the use of either a Markov random field (MRF) or an simultaneous autoregressive (SAR) model to parameterize the field based on the computation one can afford. We substantiate the suitability of the proposed method through a large number of experimentations on both simulated and real data.

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Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing

Cited by 341 publications

References 13 publications

Unsupervised image segmentation using triplet Markov fields

Unsupervised image segmentation using triplet Markov fields

Segmentation and measurement of the cortex from 3D MR images

A Learning-Based Method for Image Super-Resolution From Zoomed Observations

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