On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques

Lim, Young Won; Lee, Sang Uk

doi:10.1016/0031-3203(90)90103-r

Cited by 410 publications

(17 citation statements)

References 11 publications

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“…Since segmentation is not the topic of this paper, our current databases contain images of unoccluded objects on uniform backgrounds. Under these circumstances, a c-Means clustering and thresholding technique can be used for foreground/background separation [31]. However, for very general query by shape, foreground/background modules will be needed as a front-end to the system.…”

Section: Segmentationmentioning

confidence: 99%

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Deformable prototypes for encoding shape categories in image databases

Sclaroff

1997

Pattern Recognition

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Section: Segmentationmentioning

confidence: 99%

“…Further, tools appeared in a number of orientations and/or scales, with varying lighting. The tools were placed on a uniform background so that a simple fuzzy c-Means clustering technique could be used for foreground/background separation [31].…”

Section: Tool Image Databasementioning

confidence: 99%

Deformable prototypes for encoding shape categories in image databases

Sclaroff

1997

Pattern Recognition

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“…Perhaps the more basic among the characteristics to fit is that for cytologic features, for which standard point-wise segmentation techniques have been utilized. Segmentation methods such as the fuzzy c-Means technique proposed by Lim [7], have also been used in this work to date,…”

Section: Application Of Segmentation Methodsmentioning

confidence: 99%

Image prototype similarity matching for lymph node hemopathology

Olivieri

Vega²

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000

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This paper describes general aspects of an automated expert system for Lymph Node Hemopathology, which utilizes methods of segmentation and classification for performing image prototype similarity matching (IPSM). The expert system consists of a set of representative prototype images of a large number of histologic features required to differentiate different lymph node pathologies. A queiy case, which may consist of one or more images, is compared against each prototype set and is assigned a degree of similarity by calculating a distance metric in a multidimensional feature space. Introductory motivation to this problem is presented together with technical details of the low-level segmentation algorithms utilized. As a representative application, results are presented for cases which are dominated by cytologic characteristics.

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“…The algorithm presented in ref. [6] (Fig. 3c) analyses color channel histograms to determine thresholds for segment classification, and applies fuzzy cmeans clustering to produce a vector quantization in color space.…”

Section: Resultsmentioning

confidence: 99%

Efficient proposal distributions for MCMC image segmentation

Kostiainen¹,

Lampinen²

2004 International Conference on Image Processing, 2004. ICIP '04.

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We present methods to obtain computationally efficient proposal distributions for Bayesian reversible jump Markov chain Monte Carlo (RJMCMC) based image segmentation. The slow convergence of MCMC methods often makes them poorly suited for practical image processing applications. We show how carefully crafted proposal distributions along with certain approximations can decrease the computational cost of MCMC image segmentation to a level that is comparable with some traditional algorithms. We also discuss the interpretation of the resulting distribution of different segmentations and present experimental results.

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On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques

Cited by 410 publications

References 11 publications

Deformable prototypes for encoding shape categories in image databases

Deformable prototypes for encoding shape categories in image databases

Image prototype similarity matching for lymph node hemopathology

Efficient proposal distributions for MCMC image segmentation

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