Spatial color histogram-based image segmentation using texture-aware region merging

Lee, Ho Sub; Cho, Sung In

doi:10.1007/s11042-022-11983-4

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…These methods can be used to immediately determine the threshold, but they suffer the drawbacks of a time-consuming process and significant human error. Theoretical methods, which can be categorized as global or local methods, are based on certain threshold calculation theories [29], such as the Otsu algorithm [30,31], iteration algorithm [32], image histograms [33], entropy criterion [34], gray wolf optimizer [35], and edge detection [36]. Although these theories could facilitate threshold calculation, they are not all appropriate for clay SEM image processing due to the very small size of soil particles and pores and the difficulty in identifying contours.…”

Section: Introductionmentioning

confidence: 99%

Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay

Zhang,

Cao,

Guo

2024

Applied Sciences

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Currently, more attention has been given to the study of the microstructure of contaminated clay, while few studies have focused on the analysis of SEM images of clay from a threshold perspective. The purpose of this paper was to determine the threshold range of image pixel values for municipal waste-contaminated clay by observing and processing SEM images of municipal waste-contaminated clay under different seepage depths and concentrations. In this study, municipal waste-contaminated clay samples were obtained at different depths in a column test exhibiting different CaCO3 and CH3COOH concentrations, imaged through SEM and subjected to binarization. Three steps for determining the pixel threshold value of the SEM images were described, including preprocessing and binarizing the SEM images in Image Pro Plus 6.0 (IPP) and morphological processing by the Pores (Particles) and Cracks Analysis System V2.3 (PCAS). The apparent void ratio of contaminated clay and the fractal dimension of clay particles were applied as evaluation parameters. The determined effective threshold range of the image pixel values of municipal waste-contaminated clay was 110–140. The accuracy of this method was verified in three aspects: the relationship between the threshold value and the apparent void ratio, the variation in pore blockage with seepage depth, and the characteristics of the compaction factor with seepage depth and pollutant concentration.

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Section: Introductionmentioning

confidence: 99%

Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay

Zhang,

Cao,

Guo

2024

Applied Sciences

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Enhanced K-means Color Clustering Based on SLIC Superpixels Merging incorporated within the Entomology Software: AInsectID

Sadia,

Alam

2024

2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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A Variational Level Set Image Segmentation Method via Fractional Differentiation

Liu

Wang

et al. 2022

Fractal Fract

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To solve the issues with conventional level set segmentation algorithms, which are sensitive to the initial contours and less noise-resistant, a segmentation model based on the coupling of texture information and structural information is developed. In this model, a rotation invariant mask produced by fractional-order differentiation is used to first describe the image's global information. Then, the power function of the energy generalization function is solved by applying factorization theory, and for each pixel of the image, not only its information but also its surrounding pixel information is taken into account and integrated into the energy generalization function via weight scaling. At the same time, the L2 norm of the fractional-order image and the difference from the fitted image are used to generate the energy generalization function of the model. The final results of this study demonstrate that the proposed model achieved a better segmentation performance than the current active contour models in terms of robustness to Gaussian noise and pretzel noise, as well as the segmentation accuracy and algorithm running time. These results were obtained in synthetic images, real images, and natural images.

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Spatial color histogram-based image segmentation using texture-aware region merging

Cited by 3 publications

References 43 publications

Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay

Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay

Enhanced K-means Color Clustering Based on SLIC Superpixels Merging incorporated within the Entomology Software: AInsectID

A Variational Level Set Image Segmentation Method via Fractional Differentiation

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