Energy Minimization With One Dot Fuzzy Initialization for Marine Oil Spill Segmentation

Ren, Peng; Xu, Min; Yu, Yunhua; Chen, Fang; Jiang, Xiangyuan; Yang, Erfu

doi:10.1109/joe.2018.2842538

Cited by 17 publications

(12 citation statements)

References 31 publications

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“…This method effectively reduces a large number of labeled data for neural network training. The one dot fuzzy initialization method (ODFI) [81] that the fuzzy connecting between any initial point and the remaining pixels leads to a physically uniform region which is consistent with the minimization of the initial energy. This method can obtain relatively complete oil spill area.…”

Section: Discussionmentioning

confidence: 99%

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019

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This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

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Section: Discussionmentioning

confidence: 99%

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019

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“…It is worth noting that the results of using fuzzy connectedness alone are easily disturbed by noise in medical images. In recent research, fuzzy connectedness has usually been integrated with other segmentation methods [47]. Both the local and global phenomena of imaging procedures are considered.…”

Section: B Fuzzy Connectednessmentioning

confidence: 99%

A Novel Framework for Improving Pulse-Coupled Neural Networks With Fuzzy Connectedness for Medical Image Segmentation

et al. 2020

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A pulse-coupled neural network (PCNN) is a promising image segmentation approach that requires no training. However, it is challenging to successfully apply a PCNN to medical image segmentation due to common but difficult scenarios such as irregular object shapes, blurred boundaries, and intensity inhomogeneity. To improve this situation, a novel framework incorporating fuzzy connectedness (FC) is proposed. First, a comparative study of the traditional PCNN models is carried out to analyze the framework and firing mechanism. Then, the characteristic matrix of fuzzy connectedness (CMFC) is presented for the first time. The CMFC can provide more intensity information and spatial relationships at the pixel level, which is helpful for producing a more reasonable firing mechanism in the PCNN models. Third, by integrating the CMFC into the PCNN framework models, a construction scheme of FC-PCNN models is designed. To illustrate this concept, a general solution that can be applied to different PCNN models is developed. Next, the segmentation performances of the proposed FC-PCNN models are evaluated by comparison with the traditional PCNN models, the traditional segmentation methods, and deep learning methods. The test images include synthetic and real medical images from the Internet and three public medical image datasets. The quantitative and visual comparative analysis demonstrates that the proposed FC-PCNN models outperform the traditional PCNN models and the traditional segmentation methods and achieve competitive performance to the deep learning methods. In addition, the proposed FC-PCNN models have favorable capability to eliminate inference from surrounding artifacts.

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“…Before sphericity and roundness measurement, the main problem is how to extract each proppant accurately. Fuzzy connectedness algorithm [25]- [27] is a very effective method in image segmentation, and it is introduced to accomplish this task.…”

Section: A Proppant Extractionmentioning

confidence: 99%

“…To be consistent with the process of the manual measurement method, an effective and automatic proppant extraction method should be selected under comprehensive consideration. Based on the principle of avoiding human interaction and relatively high accuracy of target extraction, a proppant extraction method based on fuzzy connectedness algorithm [25]- [27] is introduced to extract each proppant from the micrograph to achieve a higher accuracy of proppant extraction without human interaction. Then, for the determination of sphericity and roundness, instead of calculating through some relevant parameters such as the curvature of corner points, it is accomplished by shape similarity measurement in the visual concept, which is consistent with the process of the manual measurement method.…”

Section: Introductionmentioning

confidence: 99%

Fracturing Proppant Quality Estimation Based on Fuzzy Connectedness and Shape Similarity

Lyu

Liu

et al. 2020

IEEE Access

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To improve the accuracy and efficiency of measuring fracturing proppant sphericity and roundness, a measurement scheme is designed by combining fuzzy connectedness and shape similarity. Firstly, in the proppant images collected by the microscope, according to the color characteristics of the test placement plane's hole and proppant, the region of each proppant and that of the hole without proppant are marked separately. Secondly, based on the feature of holes without proppant, the statistical method of labeling connected components is used to only reserve all the proppant regions. Thirdly, select a part of the region of each proppant as the seed region to calculate the fuzzy connectedness map between seed regions and other regions. Fourthly, select the appropriate connectedness threshold for each proppant based on histogram of fuzzy connectedness map of each proppant region to obtain the accurate region of each proppant. Finally, the shape similarity measurement method based on Hu moment is used to determine the sphericity and roundness of each proppant by measuring the similarity between the region of each proppant and that of 20 particles in the standard template. The experimental results show that the proposed scheme is consistent with manual measurement flow, and can avoid the human labor, meanwhile, the accuracy of measurement is basically consistent with the results of manual measurement.

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Energy Minimization With One Dot Fuzzy Initialization for Marine Oil Spill Segmentation

Cited by 17 publications

References 31 publications

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

A Novel Framework for Improving Pulse-Coupled Neural Networks With Fuzzy Connectedness for Medical Image Segmentation

Fracturing Proppant Quality Estimation Based on Fuzzy Connectedness and Shape Similarity

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