Method of synthesized phase objects for pattern recognition: matched filtering

Yezhov, Pavel V.; Kuzmenko, Alexander V.; Kim, Jin‐Tae; Smirnova, T. V.

doi:10.1364/oe.20.029854

Cited by 11 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Curves (Figure 11a) demonstrate the above-written method for both conventional and SPO methods. As shown in [16], the SPOmethod demonstrates a faster diminution of the curve with increase in distortions (in the given case, with increase in the rotation angle), δ-like shape of a recognition signal, and higher values of SNR about 20.2 dB for the autocorrelation (Figure 12b); for the angle α = 5°, the signal is absent (Figure 13b). The curves in Figure 11b show variations in SNRs of the correlation functions with increase in the rotation angle for comparison objects for the conventional (light circles) and SPO (dark circles) methods at the Fourier-Mellin rotation invariant recognition.…”

Section: Computational Experimentsmentioning

confidence: 71%

“…For functions of the type φ(x, y) = exp(iϕ(x, y)) that are introduced in the objective plane of a correlator with the help of SLM, such grating is formed by means of the adding of a linear phase 2π(xu 0 + yϑ 0 ) to the phase ϕ(x, y). The spatial separation of the recognition signal and noise components in the correlation plane by the covering of a synthesized filter in the Fourier plane by a phase grating was demonstrated in [16], but the increase in SNR of the recognition signal by means of the covering of the recognition objects in the objective plane of a VL-correlator by a phase grating is made for the first time by us. The axis, relative to which the spectrum is shifted, passes through the centers of the objective and Fourier planes.…”

Section: Comparison Of the Spo And Conventional Methods Of Recognitiomentioning

confidence: 99%

“…The latter (its distribution of phases) is calculated with the help of the known iterative Fourier transform (IFT) algorithm [13]. In this case, the problem of recognition of amplitude objects, which belong to arbitrary classes, is reduced to the problem of recognition of phase objects of only one type [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Method of Synthesized Phase Objects in the Optical Pattern Recognition Problem

Yezhov¹,

Ostroukh²,

Kim³

et al. 2016

Pattern Recognition - Analysis and Applications

Self Cite

View full text Add to dashboard Cite

To solve the pattern recognition problem, a method of synthesized phase objects (SPOmethod) is suggested. The essence of the suggested method is that synthesized phase objects are used instead of real amplitude objects. The former is object-dependent phase distributions calculated using the iterative Fourier transform algorithm. The method is experimentally studied with an optical-digital Vanderlugt and joint Fourier transform 4F-correlators. The development of the SPO-method for the rotation invariant pattern recognition is considered as well. We present the comparative analysis of recognition results with the use of the conventional and proposed methods, estimate the sensitivity of the latter to distortions of the structure of objects, and determine the applicability limits. It is demonstrated that the SPO-method allows one: (a) to simplify the procedure of choice of recognition signs (criteria); (b) to obtain one-type δ-like recognition signals irrespective of the type of objects; and (c) to improve the signal-to-noise ratio for correlation signals by 20-30 dB on the average. To introduce recognition objects in a correlator, we use SLM LC-R 2500 and SLM HEO 1080 Pluto devices.

show abstract

Section: Computational Experimentsmentioning

confidence: 71%

Section: Comparison Of the Spo And Conventional Methods Of Recognitiomentioning

confidence: 99%

See 1 more Smart Citation

Method of Synthesized Phase Objects in the Optical Pattern Recognition Problem

Yezhov¹,

Ostroukh²,

Kim³

et al. 2016

Pattern Recognition - Analysis and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we propose a new TCAI method based on matched filtering (MF) [ 20 , 21 ] and convolutional neural network (CNN) [ 22 , 23 , 24 ]. By MF operation on the back signal, the signal-to-noise ratio (SNR) is improved and the spike pulses corresponding to different imaging planes are divided and extracted.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network

Chen

Luo

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.

show abstract

“…The possibility of optically or digitally processing the light coming from an object or from optical systems allows both studying the properties of the object itself or the developing of areas such as three-dimensional (3D) surface optical imaging [2], pattern recognition [3], and optical security [4], to mention just few of them. Optical processing systems can be divided into two categories: experimental [5][6][7][8][9][10][11][12][13][14] and virtual [15][16][17].…”

Section: Introductionmentioning

confidence: 99%