Logarithmically Pre-processed EMACH Filter for Enhanced Performance in Target Recognition

Rehman, Saad; Akin, Bilal; Javed, Younus; Amin, Shehzad; Young, Rupert

doi:10.1007/s13369-012-0452-6

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…Correlation filters date back to around three decades, when they were introduced primarily for object recognition [11]. Over the years, improvement in the accuracy of the correlation filters has been made, using different optimization methods [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Masood

Zafar

Ali

et al. 2021

Computational and Mathematical Methods in Medicine

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In the past few decades, the field of image processing has seen a rapid advancement in the correlation filters, which serves as a very promising tool for object detection and recognition. Mostly, complex filter equations are used for deriving the correlation filters, leading to a filter solution in a closed loop. Selection of optimal tradeoff (OT) parameters is crucial for the effectiveness of correlation filters. This paper proposes extended particle swarm optimization (EPSO) technique for the optimal selection of OT parameters. The optimal solution is proposed based on two cost functions. The best result for each target is obtained by applying the optimization technique separately. The obtained results are compared with the conventional particle swarm optimization method for various test images belonging from different state-of-the-art datasets. The obtained results depict the performance of filters improved significantly using the proposed optimization method.

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Section: Introductionmentioning

confidence: 99%

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Masood

Zafar

Ali

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Various variants of correlation filters with enhanced performance have been proposed by many researchers in the past few decades [9][10][11][12][13]. These filters are efficiently used in many applications such as biomedical, defense and security applications.…”

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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“…To augment each other, optimal MACH filter of (15) has already been combined with logmap transformation to achieve a new filter LMACH [27] in past decade. We have recently proposed a new filter LEMACH [37] which implements logarithmic preprocessing before maximising J EMACH h EMACH in (20).…”

Section: Preprocessing Stage B: Logmap Transformationmentioning

confidence: 99%

Composite filtering strategy for improving distortion invariance in object recognition

Awan

Rehman

Bakhshi

2018

IET Image Processing

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Logarithmically Pre-processed EMACH Filter for Enhanced Performance in Target Recognition

Cited by 10 publications

References 17 publications

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Hardware design of correlation filters for target detection

Composite filtering strategy for improving distortion invariance in object recognition

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