A double-threshold image binarization method based on edge detector

Chen, Qiang; Sun, Quansen; Heng, Pheng‐Ann; De-shen, Xia

doi:10.1016/j.patcog.2007.09.007

Cited by 110 publications

(55 citation statements)

References 15 publications

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“…An adaptive thresholding-based edge detection method using morphological operators is presented in [46]. A double-threshold image binarization method based on the edge detector was proposed in [47] which is based on the edge and intensity information. Image binarization is done in two stages using high and low threshold values.…”

Section: Methods For Threshold Selection For Image Binarization And/omentioning

confidence: 99%

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Khunteta

Ghosh

2014

Advances in Fuzzy Systems

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An edge is a set of connected pixels lying on the boundary between two regions in an image that differs in pixel intensity. Accordingly, several gradient-based edge detectors have been developed that are based on measuring local changes in gray value; a pixel is declared to be an edge pixel if the change is significant. However, the minimum value of intensity change that may be considered to be significant remains a question. Therefore, it makes sense to calculate the edge-strength at every pixel on the basis of the intensity gradient at that pixel point. This edge-strength gives a measure of the potentiality of a pixel to be an edge pixel. In this paper, we propose to use a set of fuzzy rules to estimate the edge-strength. This is followed by selecting a threshold; only pixels having edge-strength above the threshold are considered to be edge pixels. This threshold is selected such that the overall probability of error in identifying edge pixels, that is, the sum of the probability of misdetection and the probability of false alarm, is minimum. This minimization is achieved via particle swarm optimization (PSO). Experimental results demonstrate the effectiveness of our proposed edge detection method over some other standard gradient-based methods.

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Section: Methods For Threshold Selection For Image Binarization And/omentioning

confidence: 99%

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Khunteta

Ghosh

2014

Advances in Fuzzy Systems

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“…Most of these algorithms rely either on statistical methods (for example Bayes classifier, maximum likelihood [7,18,19] and moment preservation [37]), or on signal processing (for example maximization of the entropy of the image [1,17], minimization of the variance between the object and the background [27] and the Hadamard transform [3]). Other approaches are based on edge detection algorithms [5,14,15,28,39], on fuzzy classification [6] or on multi scale [34]. Some local approaches are based on the decomposition of an image by means of a quad-tree structure [11,10].…”

Section: Related Workmentioning

confidence: 99%

Document Binarization Based on Connected Operators

Naegel¹,

Wendling²

2009

2009 10th International Conference on Document Analysis and Recognition

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“…However, the proposed approach can still find the optimal segmentation results, which shows the robustness of our approach. [16]. (b) Segmentation result based on distances learned according to [15].…”

Section: Image Segmentationmentioning

confidence: 99%

Distance Learning Based on Convex Clustering

Yang

Latecki

Gross

2009

Advances in Visual Computing

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Abstract. Clustering has been among the most active research topics in machine learning and pattern recognition. Though recent approaches have delivered impressive results in a number of challenging clustering tasks, most of them did not solve two problems. First, most approaches need prior knowledge about the number of clusters which is not practical in many applications. Second, non-linear and elongated clusters cannot be clustered correctly. In this paper, a general framework is proposed to solve both problems by convex clustering based on learned distance. In the proposed framework, the data is transformed from elongated structures into compact ones by a novel distance learning algorithm. Then, a convex clustering algorithm is used to cluster the transformed data. Presented experimental results demonstrate successful solutions to both problems. In particular, the proposed approach is very suitable for superpixel generation, which are a common base for recent high level image segmentation algorithms.

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A double-threshold image binarization method based on edge detector

Cited by 110 publications

References 15 publications

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Document Binarization Based on Connected Operators

Distance Learning Based on Convex Clustering

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