Multisensor image segmentation using Dempster-Shafer fusion in Markov fields context

Bendjebbour, A.; Delignon, Yves; Fouque, L.; Samson, Vincent; Pieczynski, Wojciech

doi:10.1109/36.942557

Cited by 90 publications

(66 citation statements)

References 34 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An integration of different sensors, some of which being possibly ''evidential'' [2], is an another perspective for further investigation of TMF. Finally, extension of the rectangular lattice considered here to more general ''Bayesian networks'' [13] could also be viewed.…”

Section: Discussionmentioning

confidence: 99%

“…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. Another possibility is to use the general ''Iterative Conditional Estimation'' (ICE [25]) method, which has given good results in different classical HMF situations [4,7,10,16,22,23,30], and in more complex Markov models with a Dempster-Shafer fusion [2] or fuzzy hidden fields [31]. Moreover, first applications of ICE in a simple TMF context also gave promising results [27].…”

Section: Parameter Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised image segmentation using triplet Markov fields

Benboudjema¹,

Pieczynski²

2005

Computer Vision and Image Understanding

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Unsupervised image segmentation using triplet Markov fields

Benboudjema¹,

Pieczynski²

2005

Computer Vision and Image Understanding

View full text Add to dashboard Cite

show abstract

“…This strategy has been used by Pichler et al [6], [7] with their feature contrast method and indirectly by many authors who implement fuzzy logic-based fusion procedures [19]- [22]. In a similar perspective, some researchers have underlined the fact that data gathered form many sensors can be fused together with the help of Dempster-Shafer theory of evidence [23], [24]. Also, feature weighting is what back-propagation training algorithms (neural networks) are meant to do [14], [15].…”

mentioning

confidence: 99%

Segmentation Framework Based on Label Field Fusion

Jodoin

Mignotte

Rosenberger³

2007

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-In this paper, we put forward a novel fusion framework that mixes together label fields instead of observation data as is usually the case. Our framework takes as input two label fields: a quickly estimated and to-be-refined segmentation map and a spatial region map that exhibits the shape of the main objects of the scene. These two label fields are fused together with a global energy function that is minimized with a deterministic iterative conditional mode algorithm. As explained in the paper, the energy function may implement a pure fusion strategy or a fusion-reaction function. In the latter case, a data-related term is used to make the optimization problem well posed. We believe that the conceptual simplicity, the small number of parameters, the use of a simple and fast deterministic optimizer that admits a natural implementation on a parallel architecture are among the main advantages of our approach. Our fusion framework is adapted to various computer vision applications among which are motion segmentation, motion estimation and occlusion detection.

show abstract

“…For this purpose, fuzzy c-means (FCM) clustering is used to represent the grey levels as fuzzy sets [3]. Bendjebbour et al [4] proposed a probabilistic model where the frame of discernment contained the individual clusters and the ignorance that was modeled by the union of all individual clusters. In that work, the authors derived the mass value of ignorance from the mixture of distributions of the individual clusters composing it.…”

Section: Introductionmentioning

confidence: 99%

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Jiang¹,

Yan

Luo

et al. 2015

INT J COMPUT COMMUN

View full text Add to dashboard Cite

Dempster-Shafer theory of evidence has been widely used in many data fusion application systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, an improved method to determine the similarity measure between generalized fuzzy numbers is presented. The proposed method can overcome the drawbacks of the existing similarity measures. Then, we propose a new method for obtaining basic probability assignment (BPA) based on the proposed similarity measure method between generalized fuzzy numbers. Finally, the efficiency of the proposed method is illustrated by the classification of Iris data.

show abstract

Multisensor image segmentation using Dempster-Shafer fusion in Markov fields context

Cited by 90 publications

References 34 publications

Unsupervised image segmentation using triplet Markov fields

Unsupervised image segmentation using triplet Markov fields

Segmentation Framework Based on Label Field Fusion

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Contact Info

Product

Resources

About