scholarly journals Adaptive multimodal biometric fusion algorithm using particle swarm optimization and belief functions

2016 ◽  
Author(s):  
L. Mezai ◽  
F. Hachouf
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Belief Functions ◽  
Fusion Algorithm ◽  
Swarm Optimization ◽  
Biometric Fusion

scholarly journals COMPRESSIVE SENSING IMAGE FUSION BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 785-790
Author(s):  
X. Li ◽  
J. Lv ◽  
S. Jiang ◽  
H. Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Image Fusion ◽  
Compressive Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Fusion Algorithm ◽  
Swarm Optimization ◽  
Fusion Coefficient

In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. We propose a new method of image fusion that utilizes HIS transformation and the recently developed theory of compressive sensing that is called HIS-CS image fusion. In this algorithm, the particle swarm optimization algorithm is used to select the fusion coefficient ω. In the iterative process, the image fusion coefficient ω is taken as particle, and the optimal value is obtained by combining the optimal objective function. Then we use the compression-aware weighted fusion algorithm for remote sensing image fusion, taking the coefficient ω as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.

scholarly journals Multifocus Image Fusion Based on Multiresolution Transform and Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3281-3285 ◽  
Author(s):  
Yu Shu Liu ◽  
Ming Yan Jiang ◽  
Chuan Zhu Liao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Fusion ◽  
High Frequency ◽  
Particle Swarm ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Swarm Optimization ◽  
Fused Image ◽  
Multiresolution Transform

In order to get an image with every object in focus, an image fusion process is required to fuse the images under different focal settings. In this paper, a novel multifocus image fusion algorithm based on multiresolution transform and particle swarm optimization (PSO) is proposed. Firstly the source images are decomposed into lowpass subbands coefficients and highpass subbands coefficients by the nonsubsampled contourlet transform (NSCT). Then, different fusion rules are applied for low and high frequency NSCT coefficients. Finally the fused image is reconstructed by the inverse NSCT transform. The experiment results demonstrate that the proposed method is effective and can provide better performance than the method based on the wavelet transform and the nonsubsampled contourlet transform.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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