• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.9
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• pp.66-75
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• 1999

Image segmentation is the process where an image is segmented into regions that are set of homogeneous pixels. The result has a ciritical effect on accuracy of image understanding. In this paper, an Markov random field (MRF) image segmentation is proposed using genetic algorithm(GA). We model an image using MRF which is resistant to noise and blurring. While MRF based methods are robust to degradation, these require accurate parameter estimation. So GA is used as a segmentation algorithm which is effective at dealing with combinatorial problems. The efficiency of the proposed method is shown by experimental results with real images and application to automatic vehicle extraction system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.10
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• pp.964-971
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• 1991

In DPCM coder which has flat notse distributions, reconstruction noises might be shaped or transferred to hign frequency portion using the relevant noise reduction loop under the constraints of image compression. Noise shaping filter directly in loop can be used, but we shows the complete characteristics of signal blurred DPCM coder in view of data compression and noise processing. Equi weighting in bluming procedure of prefilter is introduced and the blurred signal is restored by debiurning postfilter. Noise shaping tegion on two dimensional frequency domain depends upon the size of masking filter. In spite of its noise increments by the signal blurring, the algonthm is effective for the visually relevant coding which has low pass freqency properties.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.825-828
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• 2006

The most commonly used techniques for image magnification are interpolation based. However, the magnified images produced by this technique often appear blocking and blurring phenomenon when the image is enlarged. In this paper, we enhanced image magnification algorithm using edge information. The proposed algorithm not used interpolation based but by using sub-band of input image in extrapolation. According to mapping relationship in pyramid, we calculated up-band information to magnify. In experiments, the proposed model shows solved the problem of image loss like the blocking and blurring phenomenon. As the result, it is faster and higher resolution than traditional magnification algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.662-674
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• 1997

In order to post process the vector-quantized images employing the theory of projections onto convex sets or the constrained minimization technique, the the projector onto QCS(quantization constraint set) as well as the filter that smoothes the lock boundaries should be investigated theoretically. The basic idea behind the projection onto QCS is to prevent the processed data from diverging from the original quantization region in order to reduce the blurring artifacts caused by a filtering operation. However, since the Voronoi regions in order to reduce the blurring artifacts caused by a filtering operation. However, since the Voronoi regions in the vector quantization are arbitrarilly shaped unless the vector quantization has a structural code book, the implementation of the projection onto QCS is very complicate. This paper mathematically analyzes the projection onto QCS from the viewpoit of minimizing the mean square error. Through the analysis, it has been revealed that the projection onto a subset of the QCS yields lower distortion than the projection onto QCS does. Searching for an optimal constraint set is not easy and the operation of the projector is complicate, since the shape of optimal constraint set is dependent on the statistical characteristics between the filtered and original images. Therefore, we proposed a hyper-cube as a constraint set that enables a simple projection. It sill be also shown that a proper filtering technique followed by the projection onto the hyper-cube can reduce the quantization distortion by theory and experiment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2529-2543
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• 2019

Scanning electron microscopy (SEM) image can link with the microscopic world through reflecting interaction between electrons and materials. The SEM images are easily subject to blurring distortions during the imaging process. Inspired by the fact that dark channel prior captures the changes to blurred SEM images caused by the blur process, we propose a method to evaluate the SEM images sharpness based on the dark channel prior. A SEM image database is first established with mean opinion score collected as ground truth. For the quality assessment of the SEM image, the dark channel map is generated. Since blurring is typically characterized by the spread of edge, edge of dark channel map is extracted. Then noise is removed by an edge-preserving filter. Finally, the maximum gradient and the average gradient of image are combined to generate the final sharpness score. The experimental results on the SEM blurred image database show that the proposed algorithm outperforms both the existing state-of-the-art image sharpness metrics and the general-purpose no-reference quality metrics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.113-122
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• 2005

In this paper, we propose a multi-watermarking algorithm to satisfy two purposes: fragility against malicious attacks and robustness against non-malicious attacks. The algorithm can be used for image authentication using coefficients of Discrete Wavelet Transform(DWT). In the proposed method, watermarks are generated by combining binary image with some features extracted from the subband LL3, and then they are embedded into both the spatial and frequency domain. That is, on the spatial domain they are embedded into the Least Significant Bit(LSB) of all pixels of image blocks, and on the frequency domain the coefficients of the subband LH2 and HL2 are adjusted according to the watermarks. Thus the algorithm not only resists malicious attack but also permits non-malicious attacks such as blurring, sharpening, and JPEG compression.

• The Journal of Korean Medicine
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• v.44 no.1
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• pp.108-116
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• 2023

Objectives: This study is to report the usefulness of ultrasound in diagnosing plantar fasciitis and the effectiveness of ultrasound-guided bee venom pharmacopuncture as a treatment of it. Methods: A 61-year-old woman suffered from plantar fasciitis on her right foot for 1 month. The sonographic findings were fascial thickening, blurring of perifascial border and perifascial effusion. The ultrasound-guided bee venom pharmacopuncture for 6 times and conventional Korean medicine therapies like acupuncture and moxibustion for 12 times in 5 weeks. Numeric rating scale (NRS) and foot function index(FFI) was used to evaluate the pain and its progress. and thickness of plantar fascia was measured by sonography every week. Results: After treatments, NRS and FFI were reduced from 10 to 1 and from 190 to 72. The thickness of fascia was reduced from 0.43cm to 0.40. Blurring and effusion of perifascial border were also improved. Conclusion: This report suggests that the Ultrasound-guided Bee Venom Pharmacopuncture is effective for Plantar Fasciitis. Ultrasonography could be one of the most valuable items in the clinical practice of Korean medicine doctors who seek minimally invasive treatment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.207-209
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• 2021

In recent years, with the development of artificial intelligence and IoT technology, automation and unmanned technology are in progress in various fields, and the importance of image processing such as object tracking, medical images and object recognition, which are the basis of this, is increasing. In particular, in systems requiring detailed data processing, noise reduction is used as a pre-processing step, but the existing algorithm has a disadvantage that blurring occurs in the filtering process. Therefore, in this paper, we propose a filter algorithm using modified spatial weights to minimize information loss in the filtering process. The proposed algorithm uses mask matching to remove AWGN, and obtains the output of the filter by adding or subtracting the output of the modified spatial weight. The proposed algorithm has superior noise reduction characteristics compared to the existing method and reconstructs the image while minimizing the blurring phenomenon.

• Current Optics and Photonics
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• v.8 no.2
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• pp.127-137
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• 2024

The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags' coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the three-dimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.