• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.7
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• pp.656-665
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• 2015

In this paper, we improved the clarity and quality of the radar imaging by applying motion compensation for time-frequency transform in B-ISAR imaging. The proposed motion compensation algorithm using UWB radar is verified. B-ISAR algorithm procedure and time-frequency transform for improved motion compensation are provided for theoretical ground. The image was created by a UWB Radar B-ISAR imaging algorithm method. Also, creating a B-ISAR imaging algorithm for motion compensation of time-frequency transformation method was used. The B-ISAR Imaging algorithm is implemented using STFT(Short-Time Fourier Transform), GWT(Gabor Wavelet Transform), and WVD(Wigner-Ville Distribution) approaches. The performance of STFT is compared with the GWT and WVD algorithms. It is found that the WVD image shows more clarity and decreased spread phenomenon than other methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.175-182
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• 2012

The scattered radiation always occurs when X-ray strikes the object. To absorb the scattered X-rays, the antiscatter grids are used, however these grids images are superimposed in the projection radiography images. When those images are displayed on the monitor, moir$\acute{e}$ patterns are overlapped over the images and disturb the anatomical informations. Most of the researches performed to date removed the grid noises by calculating or observing those frequencies in one dimensional frequency domain, two dimensional wavelet transform or Fourier transform. Those methods filtered not only the grid noises but also diagnostic informations. In this paper, we proposed the combined wavelet packet-Fourier method to remove the grid artifact in CR images. For the phantom image, the proposed method achieved from 5.2 to 7.4 dB better than others in SNR and for CR images by rejecting the grid noise bands effectively while leaving the remaining bands unchanged, the loss of images could get minimal results.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.3
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• pp.251-257
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• 2017

In this paper, an algorithm for detecting defects in thin-film-transistor liquid-crystal display (TFT-LCD) cell images is presented. TFT-LCD cell images typically contain periodic cell patterns that make it difficult to detect defects. We propose an efficient and powerful algorithm for eliminating the cell patterns using magnitude spectrum analysis. The first step was to obtain a spectrum for a cell image using the Fourier transform while eliminating larger coefficients using an adaptive filter. Next, an image without the cell pattern was obtained by using the inverse Fourier transform. Finally, the defect pixels were detected using the STD algorithm. The validity of the proposed method was investigated using real TFT-LCD cell images. The experimental results indicate that the proposed technique is extremely effective for detecting defects in TFT-LCD cell images.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.6
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• pp.955-963
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• 2008

In this paper, we propose an improved practical encryption and fault-tolerance decryption method using a non-negative value key and random function obtained with a white noise by using iterative phase wrapping method. A phase wrapping operating key, which is generated by the product of arbitrary random phase images and an original phase image. is zero-padded and Fourier transformed. Fourier operating key is then obtained by taking the real-valued data from this Fourier transformed image. Also the random phase wrapping operating key is made from these arbitrary random phase images and the same iterative phase wrapping method. We obtain a Fourier random operating key through the same method in the encryption process. For practical transmission of encryption and decryption keys via Internet, these keys should be intensity maps with non-negative values. The encryption key and the decryption key to meet this requirement are generated by the addition of the absolute of its minimum value to each of Fourier keys, respectively. The decryption based on 2-f setup with spatial filter is simply performed by the inverse Fourier transform of the multiplication between the encryption key and the decryption key and also can be used as a current spatial light modulator technology by phase encoding of the non-negative values. Computer simulations show the validity of the encryption method and the robust decryption system in the proposed technique.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.6
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• pp.178-188
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• 1996

In this paper, we propose a new opto-digital object recognition system which has rotation, scale, and shift invariant characteristics. The fourier power spectrum of the object image is modified to get shift invariance. The log-polar transform is used for rotation and scale invariance. And the decision of similarities is performed by nonlinear joint transform correlator (NJTC) that can control the ratio of phase and amplitude signals. Experimental verification of th eproposed optical object recognition system is presented.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.713-718
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• 1987

In this paper, an iterative restoration algorithm from power spectral density with 1 bit sign information of real part of two dimensional Fourier transform of image corrupted by additive white Gaussian noise is proposed. This method is a modified version of image reconstruction algorithm from power spectral density. From the results of computer simulation with original 32 gray level imgae of 64x64 pixels, we can find that restorated image after each iteration converge to original image very fast, and SNR gain be at least 8[dB] after 10th iteration for corrupted image with additive white Gaussian noise.

• Journal of the Optical Society of Korea
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• v.14 no.1
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• pp.33-37
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• 2010

The joint transform correlator (JTC) has been the best known technique for pattern recognition and identification. The JTC optically compares the reference image with the sample image then examines match or non-match by acquiring a correlation peak. However, the JTC technique has some problems such as weak correlation discrimination and noise which originates from the interference fringes in the Fourier transform plane. In order to solve these problems, this paper proposes a new technique of modifications of the interference fringes by adopting special iterative filters. Experimental results are presented to show that the proposed technique can successfully improve the correlation peaks and the level of discrimination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1744-1760
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• 2021

Since correlation filter appeared in the field of object tracking, it plays an increasingly vital role due to its excellent performance. Although many sophisticated trackers have been successfully applied to track the object accurately, very few of them attaches importance to the scale and rotation estimation. In order to address the above limitation, we propose a novel method combined with Fourier-Mellin transform and confidence evaluation strategy for robust object tracking. In the first place, we construct a correlation filter to locate the target object precisely. Then, a log-polar technique is used in the Fourier-Mellin transform to cope with the rotation and scale changes. In order to achieve subpixel accuracy, we come up with an efficient surface fitting mechanism to obtain the optimal calculation result. In addition, we introduce a confidence evaluation strategy modeled on the output response, which can decrease the impact of image noise and perform as a criterion to evaluate the target model stability. Experimental experiments on OTB100 demonstrate that the proposed algorithm achieves superior capability in success plots and precision plots of OPE, which is 10.8% points and 8.6% points than those of KCF. Besides, our method performs favorably against the others in terms of SRE and TRE validation schemes, which shows the superiority of our proposed algorithm in scale and rotation evaluation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.1-11
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• 2023

In this paper, instead of using recurrent neural network, we compare a classification performance of time series imaging algorithms using convolution neural network. There are traditional algorithms that imaging time series data (e.g. GAF(Gramian Angular Field), MTF(Markov Transition Field), RP(Recurrence Plot)) in TSC(Time Series Classification) community. Furthermore, we compare STFT(Short Time Fourier Transform) algorithm that can acquire spectrogram that visualize feature of voice data. We experiment CNN's performance by adjusting hyper parameters of imaging algorithms. When evaluate with GunPoint dataset in UCR archive, STFT(Short-Time Fourier transform) has higher accuracy than other algorithms. GAF has 98~99% accuracy either, but there is a disadvantage that size of image is massive.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.1
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• pp.21-28
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• 2004

In the field of content-based image retrieval, many numerical features have been proposed for representing visual image content such as color, torture, and shape. Because the features are assumed to be independent, each of them is extracted without ny consideration of the others. In this paper, we consider the relationship between color and texture and propose a new feature called CCE(channel color energy). Simulation results with natural images show that the proposed method outperforms the conventional regular weighted comparison method and SCFT(sequential chromatic Fourier transform)-based color torture method.