Edge Detection Based on Spiking Neural Network Model

Wu, Qingxiang; McGinnity, Martin; Maguire, Liam; Belatreche, Ammar; Glackin, Brendan

doi:10.1007/978-3-540-74205-0_4

Cited by 43 publications

(40 citation statements)

References 9 publications

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“…and white due to the double threshold, and differs from that of the Sobel, displayed in output is expressed in the form of a spike-pulse rate map of the output layer, where brigh spikes than the given threshold. The network was set to iterate 100 times, which consistent (Wu et al 2007). …”

Section: Simulation Resultsmentioning

confidence: 99%

“…After a given period, the neuron is again able to integrate input to generate a spike. All the parameters used in the simulation are consistent with values known to be biologically meaningful (Wu et al 2007), and the values of the weight matrices are evaluated through simulation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The SNN output is expressed in the form of a spike-pulse rate map of the output layer, where brighter lines mean more spikes than the given threshold. The network was set to iterate 100 times, which is also biologically consistent (Wu et al 2007). …”

Section: Simulation Resultsmentioning

confidence: 99%

“…Space Sci. 33(4), 313-318 (2016) values known to be biologically meaningful (Wu et al 2007), and the values of the weight matrices are evaluated through simulation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…A simple Spiking neural network (SNN) is known to be effective for simulating a visual system since it has a modeling structure that can describe the operation of the human visual system (Wu et al 2007). It allows for rapid decoding of sensor information, as in the visual system, where massive parallel processing is possible.…”

Section: Introductionmentioning

confidence: 99%

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Edge Detection Method Based on Neural Networks for COMS MI Images

Lee¹,

Park²,

Woo³

2016

Journal of Astronomy and Space Sciences

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Communication, Ocean And Meteorological Satellite (COMS) Meteorological Imager (MI) images are processed for radiometric and geometric correction from raw image data. When intermediate image data are matched and compared with reference landmark images in the geometrical correction process, various techniques for edge detection can be applied. It is essential to have a precise and correct edged image in this process, since its matching with the reference is directly related to the accuracy of the ground station output images. An edge detection method based on neural networks is applied for the ground processing of MI images for obtaining sharp edges in the correct positions. The simulation results are analyzed and characterized by comparing them with the results of conventional methods, such as Sobel and Canny filters.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

“…Space Sci. 33(4), 313-318 (2016) values known to be biologically meaningful (Wu et al 2007), and the values of the weight matrices are evaluated through simulation.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Edge Detection Method Based on Neural Networks for COMS MI Images

Lee¹,

Park²,

Woo³

2016

Journal of Astronomy and Space Sciences

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Edge and anomaly detection of brain magnetic resonance images in a distributed environment

Chaoui

Ghomari

Meftah

2021

Int J Imaging Syst Tech

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Today, all scientific advancements related to medical image processing aim to develop a unique computational model. The latter will mimic the way humans interpret images. In the present paper, we propose a formal approach biologically inspired by the human natural vision system’s mechanisms. To that end, we use the spiking neural network model for edge detection in brain MRI images. The optimal results provided by this last step increase performance, and facilitate the process of anomaly detection. For that, we have developed a tool called Edge and Anomaly Detection of Brain MRI Images in Distributed Environment (EADBMIDE). It is tested on an MRI brain tumour dataset, which shows the effectiveness of the proposed methods. Each stage in this tool is compared separately with similar approaches in the literature. The obtained results show a significant improvement, making this a recommended tool for edge and anomaly detection methods in MRI images.

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Finding Specific Person Using Spiking Neural Network Based on Texture Features

Wang

Lin

et al. 2014

Intelligent Computing Theory

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Edge Detection Based on Spiking Neural Network Model

Cited by 43 publications

References 9 publications

Edge Detection Method Based on Neural Networks for COMS MI Images

Edge Detection Method Based on Neural Networks for COMS MI Images

Edge and anomaly detection of brain magnetic resonance images in a distributed environment

Finding Specific Person Using Spiking Neural Network Based on Texture Features

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