Image thresholding based on Ali-Silvey distance measures

Ramac, Liane C.; Varshney, Pramod K.

doi:10.1016/s0031-3203(96)00149-5

Cited by 22 publications

(6 citation statements)

References 15 publications

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“…Figures(1,2 and 3) present a sample of the testing results; each ſgure represents a real image and the segmented images with Otsu, Otsu-Gamma and Otsu-Lognormal respectively. For the purpose of evaluating the performance of the proposed methods against Otsu-Gaussian, metrics of Image uniformity, and Inter-region contrast are used as performance measures [15], [16]. Table-1 lists for each test image the threshold value for each of the three tested methods and the value of each the evaluation metric, the methods are then ranked for each image according to the value of each metric, higher value means better threshold estimation.…”

Section: Resultsmentioning

confidence: 99%

Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models

AlSaeed

Bouridane

El-Zaart

et al. 2012

2012 International Conference on Information Technology and E-Services

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Otsu's method of image segmentation is one of the best methods for threshold selection. With Otsu's method an optimum threshold is found by maximizing the between-class variance; Otsu algorithm is based on the gray-level histogram which is estimated by a sum of Gaussian distributions. In some type of images, image data does not best ſt in a Gaussian distribution model. The objective of this study is to develop and compare two modiſed versions of Otsu method, one is based on Lognormal distribution (Otsu-Lognormal), while the other is based on Gamma distribution (Otsu-Gamma); the maximum betweencluster variance is modiſed based on each model. The twoproposed methods were applied on several images and promising experimental results were obtained. Evaluation of the resulting segmented images shows that both Otsu-Gamma method and Otsu-Lognormal yield better estimation of the optimal threshold than does the original Otsu method with Gaussian distribution (Otsu).

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Section: Resultsmentioning

confidence: 99%

Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models

AlSaeed

Bouridane

El-Zaart

et al. 2012

2012 International Conference on Information Technology and E-Services

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“…It was reported by Ramac and Varshney [26] that Chang et al's relative entropy method, GRE did not perform well for some images. This was mainly due to fact that their image histograms are distributed sparsely with large gaps between two consecutive grey levels.…”

Section: Histogram Compression and Translationmentioning

confidence: 88%

“…The crucial difference between entropy thresholding and relative entropy thresholding is that the former maximises Shannon's entropy, whereas the latter minimises relative entropy. Chang et al's approach was further improved in the work of Lee et al [25] and was also extended to Ali-Silvey distance measures in the work of Ramac and Varshney [26]. In analogy with the idea that Pal and Pal extended Pun's ME approach to local entropy and joint entropy methods, Lee et al's also extended Chang et al's relative entropy approach to local relative entropy (LRE) and joint relative entropy (JRE) methods.…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

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Survey and comparative analysis of entropy and relative entropy thresholding techniques

Chang

Wang

et al. 2006

IEE Proc., Vis. Image Process.

177

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Entropy-based image thresholding has received considerable interest in recent years. Two types of entropy are generally used as thresholding criteria: Shannon's entropy and relative entropy, also known as Kullback -Leibler information distance, where the former measures uncertainty in an information source with an optimal threshold obtained by maximising Shannon's entropy, whereas the latter measures the information discrepancy between two different sources with an optimal threshold obtained by minimising relative entropy. Many thresholding methods have been developed for both criteria and reported in the literature. These two entropybased thresholding criteria have been investigated and the relationship among entropy and relative entropy thresholding methods has been explored. In particular, a survey and comparative analysis is conducted among several widely used methods that include Pun and Kapur's maximum entropy, Kittler and Illingworth's minimum error thresholding, Pal and Pal's entropy thresholding and Chang et al.'s relative entropy thresholding methods. In order to objectively assess these methods, two measures, uniformity and shape, are used for performance evaluation.

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“…However, various features of container identifiers, such as size, position, color etc., are not normalized and the shape of identifiers is impaired frequently by the environmental factors during transportation and the container breakdown. Moreover, container images include diverse colors, globally changed intensity and various types of noise, so that the selection of threshold value for image binarization is difficult using traditional methods which use distance measures [4,5].…”

Section: Extraction Of Individual Identifiersmentioning

confidence: 99%

Recognition of Identifiers from Shipping Container Images Using Fuzzy Binarization and Enhanced Fuzzy RBF Network

Kim

Cho

2006

Soft Comput

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The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. In this paper, we propose and evaluate a novel recognition algorithm for container identifiers that effectively overcomes these difficulties and recognizes identifiers from container images captured in various environments. The proposed algorithm, first, extracts the area containing only the identifiers from a container image by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper. Then a contour tracking method is applied to the binarized area in order to extract the container identifiers which are the target for recognition. This paper also proposes an enhanced fuzzy RBF network that adapts the enhanced fuzzy ART network for the middle layer. This network is applied to the recognition of extracted identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm works better on the extraction and recognition of container identifiers compared to conventional algorithms.

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Image thresholding based on Ali-Silvey distance measures

Cited by 22 publications

References 15 publications

Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models

Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models

Survey and comparative analysis of entropy and relative entropy thresholding techniques

Recognition of Identifiers from Shipping Container Images Using Fuzzy Binarization and Enhanced Fuzzy RBF Network

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