Digital image thresholding, based on topological stable-state
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Opposition-based Laplacian Equilibrium Optimizer with application in Image Segmentation using Multilevel Thresholding
2021, Expert Systems with ApplicationsA review of remote sensing applications for water security: Quantity, quality, and extremes
2020, Journal of HydrologyCitation Excerpt :entropy-based methods – determine the entropy of the two clusters and the cross-entropy between the original and binarized images (Cheng et al., 1999; Kapur et al., 1985; Rajinikanth et al., 2018; Sahoo et al., 1997; Sarkar et al., 2016), object attribute-based methods – which assess the similarity between original and binarized images (Dasgupta et al., 2018; Hertz and Schafer, 1988; Martinis et al., 2009; Pikaz and Averbuch, 1996; Pulvirenti et al., 2011), spatial methods – which use probability distributions between the pixels (Brivio et al., 2002; Cao et al., a,b,c).
Imaged based fractal characterization of micro-fracture structure in coal
2019, FuelCitation Excerpt :After the coal images treated with denoise, the gray images were segmented, this was accomplished by setting a threshold value first, and then the grayscale of the pixels which were bigger than the threshold were set as 255 or set up as 0 if the grayscale of the pixels were smaller than the threshold. This process is termed as binarization, which is essential before pore and/or fracture extraction from rock images [37,38]. Several methods have been used to determine the threshold for binarization, such as the bimodal method, porosity restriction method and the Digital Terrain Model (DTM).
Different binarization processes validated against manual counts of fluorescent bacterial cells
2016, Journal of Microbiological MethodsCitation Excerpt :In this method, groups of objects with identical intensity-values are predefined and correlated to an incremental increasing threshold level. Objects regarded as “noise”, rapidly disappear when shifting the threshold (Pikaz and Averbuch, 1996). Another comparable algorithm is the stable count method.
Optimal multi-level thresholding with membrane computing
2015, Digital Signal Processing: A Review JournalSegmentation of color lip images by optimal thresholding using bacterial foraging optimization (BFO)
2014, Journal of Computational ScienceCitation Excerpt :Indeed, image thresholding is a major operation in many image processing applications such as optical character recognition where the goal is to extract the character in a document image and then recognize it [4]. Thresholding techniques can be classified into two categories: The first category includes methods that find the optimal threshold using image histogram analysis [5]. The second category includes methods that find the optimal threshold using objective functions.