• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.147-157
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• 2013

With the advent of the digital environment that can be accessed anytime, anywhere with the introduction of high-speed network, the free distribution and use of digital content were made possible. Ironically this environment is raising a variety of copyright infringement, and product images used in the online shopping mall are pirated frequently. There are many controversial issues whether shopping mall images are creative works or not. According to Supreme Court's decision in 2001, to ad pictures taken with ham products is simply a clone of the appearance of objects to deliver nothing but the decision was not only creative expression. But for the photographer's losses recognized in the advertising photo shoot takes the typical cost was estimated damages. According to Seoul District Court precedents in 2003, if there are the photographer's personality and creativity in the selection of the subject, the composition of the set, the direction and amount of light control, set the angle of the camera, shutter speed, shutter chance, other shooting methods for capturing, developing and printing process, the works should be protected by copyright law by the Court's sentence. In order to receive copyright protection of the shopping mall images by the law, it is simply not to convey the status of the product, the photographer's personality and creativity can be recognized that it requires effort. Accordingly, the cost of making the mall image increases, and the necessity for copyright protection becomes higher. The product images of the online shopping mall have a very unique configuration unlike the general pictures such as portraits and landscape photos and, therefore, the general image watermarking technique can not satisfy the requirements of the image watermarking. Because background of product images commonly used in shopping malls is white or black, or gray scale (gradient) color, it is difficult to utilize the space to embed a watermark and the area is very sensitive even a slight change. In this paper, the characteristics of images used in shopping malls are analyzed and a watermarking technology which is suitable to the shopping mall images is proposed. The proposed image watermarking technology divide a product image into smaller blocks, and the corresponding blocks are transformed by DCT (Discrete Cosine Transform), and then the watermark information was inserted into images using quantization of DCT coefficients. Because uniform treatment of the DCT coefficients for quantization cause visual blocking artifacts, the proposed algorithm used weighted mask which quantizes finely the coefficients located block boundaries and coarsely the coefficients located center area of the block. This mask improves subjective visual quality as well as the objective quality of the images. In addition, in order to improve the safety of the algorithm, the blocks which is embedded the watermark are randomly selected and the turbo code is used to reduce the BER when extracting the watermark. The PSNR(Peak Signal to Noise Ratio) of the shopping mall image watermarked by the proposed algorithm is 40.7~48.5[dB] and BER(Bit Error Rate) after JPEG with QF = 70 is 0. This means the watermarked image is high quality and the algorithm is robust to JPEG compression that is used generally at the online shopping malls. Also, for 40% change in size and 40 degrees of rotation, the BER is 0. In general, the shopping malls are used compressed images with QF which is higher than 90. Because the pirated image is used to replicate from original image, the proposed algorithm can identify the copyright infringement in the most cases. As shown the experimental results, the proposed algorithm is suitable to the shopping mall images with simple background. However, the future study should be carried out to enhance the robustness of the proposed algorithm because the robustness loss is occurred after mask process.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.35-39
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• 2009

Purpose: Diagnostic and functional imaging softwares in Nuclear Medicine have been developed significantly. But, there are some limitations which like take a lot of time. In this article, we introduced that the basic concept of macro to help understanding macro and its application to Brain SPECT processing. We adopted macro software to SPM processing and PACS verify processing of Brain SPECT processing. Materials and Methods: In Brain SPECT, we choose SPM processing and two PACS works which have large portion of a work. SPM is the software package to analyze neuroimaging data. And purpose of SPM is quantitative analysis between groups. Results are made by complicated process such as realignment, normalization, smoothing and mapping. We made this process to be more simple by using macro program. After sending image to PACS, we directly input coordinates of mouse using simple macro program for processes of color mapping, adjustment of gray scale, copy, cut and match. So we compared time for making result by hand with making result by macro program. Finally, we got results by applying times to number of studies in 2007. Results: In 2007, the number of SPM studies were 115 and the number of PACS studies were 834 according to Diamox study. It was taken 10 to 15 minutes for SPM work by hand according to expertness and 5 minutes and a half was uniformly needed using Macro. After applying needed time to the number of studies, we calculated an average time per a year. When using SPM work by hand according to expertness, 1150 to 1725 minutes (19 to 29 hours) were needed and 632 seconds (11 hours) were needed for using Macro. When using PACS work by hand, 2 to 3 minutes were needed and for using Macro, 45 seconds were needed. After applying theses time to the number of studies, when working by hand, 1668 to 2502 minutes (28 to 42 hours) were needed and for using Macro, 625 minutes (10 hours) were needed. Following by these results, it was shown that 1043 to 1877 (17 to 31 hours were saved. Therefore, we could save 45 to 63% for SPM, 62 to 75% for PACS work and 55 to 70% for total brain SPECT processing in 2007. Conclusions: On the basis of the number of studies, there was significant time saved when we applied Macro to brain SPECT processing and also it was shown that even though work is taken a little time, there is a possibility to save lots of time according to the number of studies. It gives time on technologist's side which makes radiological technologist more concentrate for patients and reduce probability of mistake. Appling Macro to brain SPECT processing helps for both of radiological technologists and patients and contribute to improve quality of hospital service.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.3
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• pp.258-268
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• 2001

The compression method is necessarily used to send the high quality moving picture that contains a number of data in image processing. In the field of moving picture compression method, the motion estimation algorithm is used to reduce the temporal redundancy. Block matching algorithm to be usually used is distinguished partial search algorithm with full search algorithm. Full search algorithm be used in this paper is the method to compare the reference block with entire block in the search window. It is very efficient and has simple data flow and control circuit. But the bigger the search window, the larger hardware size, because large computational operation is needed. In this paper, we design the full search block matching motion estimator. Using the DCT DC values, we decide luminance. And we apply 3 bit compare-selector using bit plane to I(Intra coded) picture, not using 8 bit luminance signals. Also it is suggested that use the same selective bit for the P(Predicted coded) and B(Bidirectional coded) picture. We compare based full search method with PSNR(Peak Signal to Noise Ratio) for C language modeling. Its condition is the reference block 8$\times$8, the search window 24$\times$24 and 352$\times$288 gray scale standard video images. The result has small difference that we cannot see. And we design the suggested motion estimator that hardware size is proved to reduce 38.3％ for structure I and 30.7％ for structure II. The memory is proved to reduce 31.3％ for structure I and II.