A survey of threshold selection techniques

Weszka, Joan S.

doi:10.1016/0146-664x(78)90116-8

Cited by 441 publications

(130 citation statements)

References 6 publications

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“…A faster version of Otsu's [9][10] [11] method is implemented for improving the efficiency of computation for the optimal thresholds. A criterion for maximizing a modified between-class variance that is equivalent to the criterion of maximizing the usual betweenclass variance is proposed for image segmentation [12][13] [14].…”

Section: Resultsmentioning

confidence: 99%

“…Otsu [11]'s method implements by minimizing the weighted sum of within-class variances of the foreground and background pixels to establish an optimum threshold. Since minimization of within-class variances is tantamount to the maximization of between-class scatter, the choice of the optimum threshold can be formulated as :…”

Section: Clustering Thresholding Of Otsumentioning

confidence: 99%

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Multilevel Thresholding using PSO Clustering

Dash¹,

Nayak²

2014

IJCA

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Thresholding algorithms are quite easy and effective for bilevel thresholding but in case of multilevel thresholding, the performance becomes unreliable due to complexity in computation because the complexity will exponentially increase. In this approach, multilevel thresholding is done for comparison by taking help of Otsu's clustering method and PSO clustering method. A dendogram of gray levels is created based on histogram of an image. The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multi-level thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected number of clusters.

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Section: Resultsmentioning

confidence: 99%

Section: Clustering Thresholding Of Otsumentioning

confidence: 99%

Multilevel Thresholding using PSO Clustering

Dash¹,

Nayak²

2014

IJCA

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“…This being the case most real-time implementations use contrived situations with dark backgrounds and simply threshold the video data. Much work has been done on automated approaches to threshold selection [25,26].…”

Section: Segmentationmentioning

confidence: 99%

Video-Rate Visual Servoing for Robots

Corke¹,

Paul²

1989

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“…According to Ref. [2], most approaches are based on similarity and difference and, particularly, can be divided into di erent categories: thresholding [5], clustering [6][7][8], edge detection [9,10] and region extraction [11]. In this paper, we propose a clustering based approach.…”

Section: Introductionmentioning

confidence: 99%

A clustering fuzzy approach for image segmentation

CINQUE¹

2004

Pattern Recognition

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Segmentation is a fundamental step in image description or classiÿcation. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of ine ciency. This paper presents a clustering approach for image segmentation based on a modiÿed fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to ÿnd a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations conÿrm the validity of the approach proposed.

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A survey of threshold selection techniques

Cited by 441 publications

References 6 publications

Multilevel Thresholding using PSO Clustering

Multilevel Thresholding using PSO Clustering

Video-Rate Visual Servoing for Robots

A clustering fuzzy approach for image segmentation

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