• Journal of Fashion Business
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• v.9 no.4
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• pp.161-169
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• 2005

The purpose of this study was to examine effectiveness of congruence between brand image and advertisement model on fashion advertisement effect. 206 female college students were surveyed for this study. For this study, three hypothesis were set up as follows: First, if fashion brand image and advertisement model image are in congruence, consumers' product preference would be higher, compared to in disharmony. Second, if fashion brand image and advertisement model image are in congruence, consumers' advertisement attitudes would be higher, compared to in disharmony. Third, if fashion brand image and advertisement model image are in congruence, consumers' purchasing intention would be higher, compared to in disharmony. As the results, three all hypothesis were accepted. Based on these results, fashion marketing strategies regarding advertisement would be suggested.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.187-193
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• 2012

An image authentication scheme for gray scale image through embedding a digital watermark by quantization of Integer Wavelet Transform (IWT) coefficients of the image is proposed in this paper. Proposed method is designed to detect modification of an image and to identify tampered location of the image. To embed the watermark mid-frequency band of a second level IWT was used. An approximation of the original image based on LL band was stored in LSB bits of the pixel data as a recovery mark for restoration of the image. Watermarked image has achieved a good PSNR of 40 dB compared to original cover image. Restored image quality was also very good with a PSNR of more than 35 dB compared to unmodified watermarked image even when 25% of the received image is cropped. Thus, the proposed method ensures a proper balance between the fidelity of the watermarked image and the quality of the restored image.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.69-79
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• 1994

The conventional machine vision system which has uniform rectangular grid requires tremendous amount of computation for processing and analysing an image especially in 2-D image transfermations such as scaling, rotation and 3-D reconvery problem typical in robot application environment. In this study, the imaging system with nonuiformly distributed image sensors simulating human visual system, referred to as Ploar Exponential Grid(PEG), is compared with the existing conventional uniform rectangular grid system in terms of image resolution and computational complexity. By mimicking the geometric structure of the PEG sensor cell, we obtained PEG-like images using computer simulation. With the images obtained from the simulation, image resolution of the two systems are compared and some basic image processing tasks such as image scaling and rotation are implemented based on the PEG sensor system to examine its performance. Furthermore Fourier transform of PEG image is described and implemented in image analysis point of view. Also, the range and heading-angle measurement errors usually encountered in 3-D coordinates recovery with stereo camera system are claculated based on the PEG sensor system and compared with those obtained from the uniform rectangular grid system. In fact, the PEC imaging system not only reduces the computational requirements but also has scale and rotational invariance property in Fourier spectrum. Hence the PEG system has more suitable image coordinate system for image scaling, rotation, and image recognition problem. The range and heading-angle measurement errors with PEG system are less than those of uniform rectangular rectangular grid system in practical measurement range.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.288-293
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• 2016

Electronic Image Stabilization(EIS) is widely used as a technique for correcting a shake of an image. The case requiring the EIS function has been increased in high magnification thermal image observation on portable military equipment. Projection Algorithm(PA) for EIS is easy to implement but its performance is sensitive to the projection area. Especially, projection profiles of thermal image have very modest change and are difficult to extract image shifts between frames. In this paper, we proposed algorithm to extract a feature image for the thermal image and compared Block Matching Algorithm(BMA) with PA using our proposed feature image. When using our proposed feature image, BMA was simply implemented using FPGA's internal small memory. And we were able to obtain 30 % PSNR improved results compared to PA.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.105-111
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• 2014

Image resolution enhancement is a technique to generate high-resolution image through improving resolution of low-resolution obtained image. It is important to estimate correctly missing pixel value in low-resolution obtained image for image resolution enhancement. In this paper, multiple shortfall estimation method for image resolution enhancement is proposed. The proposed method estimate separate multiple shortfall by predictive degradation-restoration processing in sub-images of obtained image, and generate result image combining the estimated shortfall and interpolated obtained-image. Lastly, final reconstruction image is generated by deblurring of the result image. The experimental results demonstrate that the proposed method has the best results of all compared methods in objective image quality index: PSNR, SSIM, and FSIM. The quality of reconstructed image is superior to all compared methods, and the proposed method has better lower computational complexity than compared methods. The proposed method can be useful for image resolution enhancement.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.2
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• pp.15-22
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• 1980

For efficient coding of graphic image data, the statistical characteristics for both Korean lettered images and English lettered images are measurpd and co mpared. Also, the measured run length distribution is compared with the run length distribution hased on Markov model. It is shown that the measured white run length distribution is more Bike a negative - power distribution than an exponential distribution . This fact is stronger in the Korean lettered images than is the English lettered images, The performances of four typical run length codes are compared for the same set of graphic data files,, and it is shown that the codes perform better in the Korean ]entered images :hart In Eng]isle lettered images.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.39-42
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• 2000

In our study, by observing and analyzing normal liver in abdominal CT image, we estimated gray value range and generated binary image. In the binary image, we achieved the number of hole which is located between pixels. Depending on the ratio, we processed the input image to 4 kinds of mesh images to remove the noise part that has the different ratio. With the Union image of 4 kinds of mesh images, we generated the template representing general outline of liver and subtracted from the binary image so the we can represent the organ boundary to be minute. With results of proposed method, processing time is reduced compared with existing method and we compared the result image to manual image of medical specialists.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.143-146
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• 2009

There are some image hash methods, in the paper four image hash methods have been compared: FJLT (Fast Johnson- Lindenstrauss Transform), SVD (Singular Value Decomposition), NMF (Non-Negative Matrix Factorization), FP (Feature Point). From the compared result, FJLT method can't be used in the online. the search time is very slow because of the KNN algorithm. So FJLT method has been improved in the paper.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.655-661
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• 2021

Positron emission tomography (PET) images is affected by acquisition time, short acquisition times results in low gamma counts leading to degradation of image quality by statistical noise. Noise2Void(N2V) is self supervised denoising model that is convolutional neural network (CNN) based deep learning. The purpose of this study is to evaluate denoising performance of N2V for PET image with a short acquisition time. The phantom was scanned as a list mode for 10 min using Biograph mCT40 of PET/CT (Siemens Healthcare, Erlangen, Germany). We compared PET images using NEMA image-quality phantom for standard acquisition time (10 min), short acquisition time (2min) and simulated PET image (S2 min). To evaluate performance of N2V, the peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE), structural similarity index (SSIM) and radio-activity recovery coefficient (RC) were used. The PSNR, NRMSE and SSIM for 2 min and S2 min PET images compared to 10min PET image were 30.983, 33.936, 9.954, 7.609 and 0.916, 0.934 respectively. The RC for spheres with S2 min PET image also met European Association of Nuclear Medicine Research Ltd. (EARL) FDG PET accreditation program. We confirmed generated S2 min PET image from N2V deep learning showed improvement results compared to 2 min PET image and The PET images on visual analysis were also comparable between 10 min and S2 min PET images. In conclusion, noisy PET image by means of short acquisition time using N2V denoising network model can be improved image quality without underestimation of radioactivity.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.2
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• pp.26-38
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• 2021

Image coloration refers to adding plausible colors to a grayscale image or video. Image coloration has been used in many modern fields, including restoring old photographs, as well as reducing the time spent painting cartoons. In this paper, a method is proposed for colorizing grayscale images using a convolutional neural network. We propose an encoder-decoder model, adapting FusionNet to our purpose. A proper loss function is defined instead of the MSE loss function to suit the purpose of coloring. The proposed model was verified using the ImageNet dataset. We quantitatively compared several colorization models with ours, using the peak signal-to-noise ratio (PSNR) metric. In addition, to qualitatively evaluate the results, our model was applied to images in the test dataset and compared to images applied to various other models. Finally, we applied our model to a selection of old black and white photographs.