Automatic ranking of image thresholding techniques using consensus of ground truth

Panigrahi, Susant Kumar; Gupta, Shipra

doi:10.3166/ts.35.121-136

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Vieira et al [135] averaged the results of manual thresholding and two algorithms, and estimated the uncertainty of this averaged reference dataset. Panigrahi et al [136] used a similar, although more automated approach. The core idea of this method is to perform multiple segmentations on a single dataset.…”

Section: Analytical Approachmentioning

confidence: 99%

Review of porosity uncertainty estimation methods in computed tomography dataset

Jaques

Plessis

Zemek

et al. 2021

Meas. Sci. Technol.

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X-ray computed tomography is a common tool for non-destructive testing and analysis. One major application of this imaging technique is 3D porosity identification and quantification, which involves image segmentation of the analysed dataset. This segmentation step, which is most commonly performed using a global thresholding algorithm, has a major impact on the results of the analysis. Therefore, a thorough description of the workflow and a general uncertainty estimation should be provided alongside the results of porosity analysis to ensure a certain level of confidence and reproducibility. A review of current literature in the field shows that a sufficient workflow description and an uncertainty estimation of the result are often missing. This work provides recommendations on how to report the processing steps for porosity evaluation in computed tomography data using global thresholding, and reviews the methods for the estimation of the general uncertainty in porosity measurements.

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Section: Analytical Approachmentioning

confidence: 99%

Review of porosity uncertainty estimation methods in computed tomography dataset

Jaques

Plessis

Zemek

et al. 2021

Meas. Sci. Technol.

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show abstract

“…This method is error‐prone, as it relies on human observer scores. Reference [28] rectified this by proposing a novel approach to automatically generate ground truth for image binarization by consensus of different thresholding methods. The authors concluded that F‐measure, modified Hausdorff distance, and edge mismatch error are non‐redundant and distinct indicators and are useful in evaluating the performance of the algorithm on image databases.…”

Section: Literature Reviewmentioning

confidence: 99%

Content‐based image retrieval using Gaussian–Hermite moments and firefly and grey wolf optimization

Tadepalli¹,

Kollati²,

Kuraparthi³

et al. 2021

CAAI Trans on Intel Tech

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Rapid growth in the transfer of multimedia information over the Internet requires algorithms to retrieve a queried image from large image database repositories. The proposed content-based image retrieval (CBIR) uses Gaussian-Hermite moments as the low-level features. Later these features are compressed with principal component analysis. The compressed feature set is multiplied with the weight matrix array, which has the same size as the feature vector. Hybrid firefly and grey wolf optimization (FAGWO) is used to prevent the premature convergence of optimization in the firefly algorithm. The retrieval of images in CBIR is carried out in an OpenCV python environment with K-nearest neighbours and random forest algorithm classifiers. The fitness function for FAGWO is the accuracy of the classifier. The FAGWO algorithm derives the optimum weights from a randomly generated initial population. When these optimized weights are applied, the proposed algorithm shows better precision/recall and efficiency than other techniques such as exact legendre moments, Region-based image retrieval, K-means clustering and Color descriptor waveletbased texture descriptor retrieval technique. In terms of optimization, hybrid FAGWO outperformed various optimization techniques (when used alone) like Particle Swarm Optmization, Genetic Algorithm, Grey-Wolf Optimization and FireFly algorithm.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…The 3D visual images of athletes are traditionally recognized by edge detection algorithms, which capture the significant changes of brightness in the images and improve the amplitude features. Nonetheless, these algorithms cannot achieve efficient and accurate recognition [7,8].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Image Recognition of Athletes' Wrong Motions Based on Edge Detection

Wang¹

2020

JESA

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The traditional 3D visual motion amplitude tracking algorithms cannot acquire the complete contour features, not to mention the correction of wrong motions in sports training. To solve the problem, this paper designs a 3D visual image recognition method based on contourlet domain edge detection, and applies it to the recognition of athlete’s wrong motions in sports training. Firstly, the visual reconstruction and feature analysis of human motions were carried out, and the edge detection features were extracted by edge detection algorithm. Then, a 3D visual motion amplitude tracking method was proposed based on improved inverse kinematics. The simulation results show that the proposed algorithm can effectively realize the recognition of 3D visual images of athlete motions, and improve the correction and judgment ability of athlete motions.

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Automatic ranking of image thresholding techniques using consensus of ground truth

Cited by 9 publications

References 13 publications

Review of porosity uncertainty estimation methods in computed tomography dataset

Review of porosity uncertainty estimation methods in computed tomography dataset

Content‐based image retrieval using Gaussian–Hermite moments and firefly and grey wolf optimization

Three-Dimensional Image Recognition of Athletes' Wrong Motions Based on Edge Detection

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