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

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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PCNN Based Welding Seam Image Segmentation Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.155-156.861 ◽

2012 ◽

Vol 155-156 ◽

pp. 861-866 ◽

Cited By ~ 2

Author(s):

Bei Ji Zou ◽

Hao Yu Zhou ◽

Zai Liang Chen ◽

Hao Chen ◽

Guo Jiang Xin

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Segmentation Algorithm ◽

Experimental Results ◽

Spatial Proximity ◽

Variance Ratio ◽

Segmentation Method ◽

Welding Seam ◽

Pulse Coupled Neural Network ◽

Image Segmentation Algorithm

A new welding seam image segmentation method based on pulse-coupled neural network (PCNN) is presented in this paper. The method segments image by utilizing PCNN’s specific feature that the fire of one neuron can capture firing of its adjacent neurons due to their spatial proximity and intensity similarity. The method can automatically confirm the best iteration times by comparing the maximum of variance ratio and get the best segmentation results. Experimental results show that the proposed method has good performance in both results and execution efficiency.

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Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation

Multimedia Tools and Applications ◽

10.1007/s11042-020-09228-3 ◽

2020 ◽

Vol 79 (37-38) ◽

pp. 28369-28392

Author(s):

Heming Jia ◽

Xiaoxu Peng ◽

Lifei Kang ◽

Yao Li ◽

Zichao Jiang ◽

...

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Optimization Algorithm ◽

Pulse Coupled Neural Network

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An Adaptive Image Segmentation Method Based on a Modified Pulse Coupled Neural Network

Lecture Notes in Computer Science - Advances in Natural Computation ◽

10.1007/11881070_64 ◽

2006 ◽

pp. 471-474 ◽

Cited By ~ 4

Author(s):

Min Li ◽

Wei Cai ◽

Xiao-yan Li

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Segmentation Method ◽

Pulse Coupled Neural Network

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Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

Journal of Imaging ◽

10.3390/jimaging7020037 ◽

2021 ◽

Vol 7 (2) ◽

pp. 37

Author(s):

Isah Charles Saidu ◽

Lehel Csató

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Active Learning ◽

Convolutional Neural Network ◽

Medical Image ◽

Segmentation Method ◽

Semantic Image Segmentation ◽

Batch Normalization ◽

Set Up ◽

Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

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