Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition

Tehsin, Sara; Rehman, Saad; Saeed, Muhammad Omer Bin; Riaz, Farhan; Hassan, Ali; Abbas, Muhammad; Young, Rupert; Alam, Mohammad S.

doi:10.1109/access.2017.2762354

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…The values of the optimal trade-off correlation parameters can be selected randomly on the basis of experiments. These values are then optimized depending upon the dataset and application through an hierarchal particle swarm optimization algorithm by Tehsin et al [8].…”

Section: Background Reviewmentioning

confidence: 99%

Hardware design of correlation filters for target detection

Akbar¹,

Tehsin²,

Rehman³

et al. 2019

Pattern Recognition and Tracking XXX

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Correlation filters have been implemented in software and have proven very effective for automatic target detection, biometric verification and security applications. In this paper, these filters are implemented in hardware keeping in view their impotance in real time applications. Hardware implementation results are placed in comparison with results generated through software. These results vary by as little as 10-4 which is demonstrated in the experimental section. The hardware design of these filters is implemented in LabView which can be subsequently employed in real-time security applications. This design may be expanded for other advanced variants of correlation filters in future work.

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Section: Background Reviewmentioning

confidence: 99%

Hardware design of correlation filters for target detection

Akbar¹,

Tehsin²,

Rehman³

et al. 2019

Pattern Recognition and Tracking XXX

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“…Partial-aliasing correlation filters were introduced to optimise the performance by producing sharper correlation peaks [30]. Efficacy of CPR has also been exploited for human action recognition in some recent studies [31][32][33][34][35][36], where these filters have shown quite promising results.…”

Section: Latest Trends In Cpr Filtersmentioning

confidence: 99%

Composite filtering strategy for improving distortion invariance in object recognition

Awan

Rehman

Bakhshi

2018

IET Image Processing

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“…There are several particles donating a set of optimization particles which search for the best solution in a multi-dimensional search space. This algorithm finds the best optimized value for each particle by convergence [8]. The optimized value is estimated using some cost function which defines the best value for that fitness function.…”

Section: Pso Optimal the Parameters Pidmentioning

confidence: 99%

Application of Adaptive PSO and Adaptive Fuzzy Logic Controllers to Speed Control PMSM Motor Servo Systems

Hieu

Olegovich

2018

MATEC Web Conf.

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Three phases Permanent Magnet Synchronous Motors (PMSM) are non-linear resistors, resistance of stator winding, air gap flux, cross-coupling, saturation variable times and cogging torque in operation. Due to the nonlinear nature of PMSM, it is a challenge to control exactly the speed, torque and position. This paper presents two methods for speed control stabilization of the PMSM using the Adaptive Fuzzy Logic -Proportional Integral Derivative controller (AFL-PID) and Adaptive Particle Swarm Optimization -Proportional Integral Derivative controller (APSO-PID). The response results of the speed control PMSM Servo Systems use AFLC-PID and APSO-PID methods are compared and the conclusions are given.

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Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition

Cited by 19 publications

References 27 publications

Hardware design of correlation filters for target detection

Hardware design of correlation filters for target detection

Composite filtering strategy for improving distortion invariance in object recognition

Application of Adaptive PSO and Adaptive Fuzzy Logic Controllers to Speed Control PMSM Motor Servo Systems

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