Relevance of the Dempster-Shafer evidence theory for image segmentation

Chaabane, Salim Ben; Fnaiech, Farhat; Sayadi, Mounir; Brassart, É.

doi:10.1109/icscs.2009.5412578

Cited by 16 publications

(34 citation statements)

References 9 publications

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“…Later works [24,25] applied it to image segmentation, and showed that it can be superior to Bayesian theory. Evidence theory is a generalization of Bayesian theory which jointly represents inaccuracy and uncertainty information.…”

Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

Efficient Pixel-Grouping Based on Dempster’s Theory of Evidence for Image Segmentation

Scheuermann

Schlosser

Rosenhahn

2013

Computer Vision – ACCV 2012

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Abstract. In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster's theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.

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Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

Efficient Pixel-Grouping Based on Dempster’s Theory of Evidence for Image Segmentation

Scheuermann

Schlosser

Rosenhahn

2013

Computer Vision – ACCV 2012

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“…There is still no segmentation technique that can dominate the others for all kinds of color images yet [3,4]. Our interest in this study is to segment medical color images.…”

Section: Introductionmentioning

confidence: 99%

Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images

Harrabi

Braiek

2012

J Image Video Proc

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In this article, we present a new color image segmentation method, based on multilevel thresholding and data fusion techniques which aim at combining different data sources associated to the same color image in order to increase the information quality and to get a more reliable and accurate segmentation result. The proposed segmentation approach is conceptually different and explores a new strategy. In fact, instead of considering only one image for each application, our technique consists in combining many realizations of the same image, together, in order to increase the information quality and to get an optimal segmented image. For segmentation, we proceed in two steps. In the first step, we begin by identifying the most significant peaks of the histogram. For this purpose, an optimal multi-level thresholding is used based on the two-stage Otsu optimization approach. In the second step, the evidence theory is employed to merge several images represented in different color spaces, in order to get a final reliable and accurate segmentation result. The notion of mass functions, in the DempsterShafer (DS) evidence theory, is linked to the Gaussian distribution, and the final segmentation is achieved, on an input image, expressed in different color spaces, by using the DS combination rule and decision. The algorithm is demonstrated through the segmentation of medical color images. The classification accuracy of the proposed method is evaluated and a comparative study versus existing techniques is presented. The experiments were conducted on an extensive set of color images. Satisfactory segmentation results have been obtained showing the effectiveness and superiority of the proposed method.

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“…Earlier works on image segmentation using the Dempster-Shafer theory of evidence have been presented in [9][10][11][12] and in [13]. These works combined the evidence theory with either a simple threshholding [9], a decisional procedure [10], a fuzzy clustering algorithm [11], a region merging algorithm [12] or a k-means clustering algorithm [13].…”

Section: Introductionmentioning

confidence: 99%

“…These works combined the evidence theory with either a simple threshholding [9], a decisional procedure [10], a fuzzy clustering algorithm [11], a region merging algorithm [12] or a k-means clustering algorithm [13]. All cited works use the idea of DempsterShafer to fuse the informations arising from three color channels.…”

Section: Introductionmentioning

confidence: 99%

Feature Quarrels: The Dempster-Shafer Evidence Theory for Image Segmentation Using a Variational Framework

Scheuermann

Rosenhahn

2011

Computer Vision – ACCV 2010

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Abstract. Image segmentation is the process of partitioning an image into at least two regions. Usually, active contours or level set based image segmentation methods combine different feature channels, arising from the color distribution, texture or scale information, in an energy minimization approach. In this paper, we integrate the Dempster-Shafer evidence theory in level set based image segmentation to fuse the information (and resolve conflicts) arising from different feature channels. They are further combined with a smoothing term and applied to the signed distance function of an evolving contour. In several experiments we demonstrate the properties and advantages of using the DempsterShafer evidence theory in level set based image segmentation.

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Relevance of the Dempster-Shafer evidence theory for image segmentation

Cited by 16 publications

References 9 publications

Efficient Pixel-Grouping Based on Dempster’s Theory of Evidence for Image Segmentation

Efficient Pixel-Grouping Based on Dempster’s Theory of Evidence for Image Segmentation

Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images

Feature Quarrels: The Dempster-Shafer Evidence Theory for Image Segmentation Using a Variational Framework

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