CoRAE: Energy Compaction-Based Correlation Pattern Recognition Training Using AutoEncoder

Sabir, Dilshad; Butt, Muhammad Fasih Uddin; Hassan, Ali; Rehman, Saad; Mehmood, Mehwish; Aljohani, Abdulah Jeza

doi:10.1109/access.2023.3253964

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…The matched filter is arguably the most ancient and straightforward filter [5]. A wide variety of filter design approaches have been put forth in the literature to expand this fundamental concept, such as synthetic discriminant function (SDF) filter [6], phase-only filter (POF) [7][8][9][10][11][12][13][14][15][16][17], minimum average correlation energy (MACE) filter [18][19][20], maximum average correlation energy (MACH) filter [21][22][23][24][25][26][27], extended MACH filter [28][29][30][31][32][33][34], wavelet-modified MACH filter [35], maximum margin correlation filter [36], neural network-based correlation filter [37,38], and segmented composite filter [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-modified segmented phase-only filter for in-plane rotated object recognition

Kumar,

Nishchal,

Alfalou

2024

Phys. Scr.

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In this paper, we propose and implement a wavelet-modified segmented phase-only filter for in-plane rotated object recognition. The wavelet filter is combined with the segmented phase-only filter to significantly enhance the correlation capability of the optical processor. To verify the consistency of the results, we have calculated the performance measure parameters such as peak-to-correlation energy, peak-to-sidelobe ratio, discrimination ratio and signal-to-noise ratio and compared the performance with a conventional segmented phase-only filter. For carrying out the study, we have developed a dataset of different distorted objects such as alphabets, digits, vehicles, fighter planes. Through a series of experiments and case studies, we are able to prove that the proposed filter reduces the false alarm rate while increasing recognition capability.

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