Multi-modal image segmentation using a modified Hopfield neural network

Rout, Saroj Kumar; Seethalakshmy,; Srivastava, Pramod K.; Majumdar, Jharna

doi:10.1016/s0031-3203(97)00089-7

Cited by 21 publications

(9 citation statements)

References 7 publications

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“…(3) Other specific theories Other specific theories includes image segmentation method based on Neural Network [9][10][11][12][13][14][15][16][17], Image segmentation method based on support vector machine [18][19][20], image segmentation method based on graph theory [21][22], image segmentation method based on immune algorithm [23], image segmentation method based on granular computing theory [24][25][26][27], and so on.…”

Section: Segmentation Methods Based On the Specific Theorymentioning

confidence: 99%

Investigation on Digital Media Image Processing Algorithm Based on Asynchronous and Inertia Adaptive Particle Swarm Optimization

Zhang¹,

Zhou²,

Bao³

2015

IJSIP

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How to keep the original characteristics of the digital media image is quite important in the image segmentation. In this paper, we propose a new algorithm for digital media image segmentation, and it is also can be used in the image processing.The algorithm is based on asynchronous particle swarm optimization algorithm to obtain the adaptive threshold; take the inertia factor into the algorithm, the optimal threshold has been acquired for the image segmentation. Compared with other particle swarm optimization algorithm, the algorithm has the advantages of stable, easy to converge to the optimal solution, and high segmentation speed.

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Section: Segmentation Methods Based On the Specific Theorymentioning

confidence: 99%

Investigation on Digital Media Image Processing Algorithm Based on Asynchronous and Inertia Adaptive Particle Swarm Optimization

Zhang¹,

Zhou²,

Bao³

2015

IJSIP

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“…80, computed using Eqs. (16)- (18). (b) By averaging, the symmetrical distance matrix can be obtained, and the partial results are shown here.…”

Section: Training and Testingmentioning

confidence: 99%

“…Hopfield networks are one of the widely using techniques. 17,18 Campadelli et al 17 proposed two different algorithms: a Hopfield network and a single network according to the number of clusters obtained using histogram analysis. The histogram method adopts a scalespace histogram filtering technique to obtain optimal decision boundaries for the peaks in the histogram, and then the number of clusters can be obtained by histogram analysis.…”

Section: Introductionmentioning

confidence: 99%

Color image segmentation using a self-organizing map algorithm

Chen

2002

J. Electron. Imaging

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A color image segmentation methodology based on a self-organizing map (SOM) is proposed. The method developed takes into account the color similarity and spatial relationship of objects within an image. According to the features of color similarity, an image is first segmented into coarse cluster regions. The resulting regions are then treated by computing the spatial distance between any two cluster regions, and the SOM with a labeling process is applied. In this paper, the selection of the parameters for the SOM algorithm was also investigated experimentally. The experimental results show that the proposed system is feasible, and that the segmented object regions are similar to those perceived by human vision.

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“…Rout [8] and Shen [11] used HNN to find an optimal threshold surface, which is determined by interpolating the image gray levels at edge points for segmenting image into different areas.…”

Section: Introductionmentioning

confidence: 99%

Images segmentation using a modified Hopfield artificial neural network

Albanez¹,

Silva²,

Batista³

et al. 2018

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Abstract. Good image segmentation can be achieved by finding the optimum solution to an appropriate energy function. A Hopfield neural network has been shown to solve complex optimization problems fast, but it only guarantees convergence to a local minimum of the optimization function. This paper proposed a little modification in the Hopfield neural network, the experimental results were made with proposed model for gray-scale images segmentation on synthetic and satellite images showing its effectiveness.

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Multi-modal image segmentation using a modified Hopfield neural network

Cited by 21 publications

References 7 publications

Investigation on Digital Media Image Processing Algorithm Based on Asynchronous and Inertia Adaptive Particle Swarm Optimization

Investigation on Digital Media Image Processing Algorithm Based on Asynchronous and Inertia Adaptive Particle Swarm Optimization

Color image segmentation using a self-organizing map algorithm

Images segmentation using a modified Hopfield artificial neural network

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