• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.563-564
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• 2006

A single photon counting type image sensor which is applicable for medical diagnosis with digitally obtained image and industrial purpose has been designed using $0.25{\mu}m$triple well CMOS process.

• Journal of Distribution Science
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• v.12 no.5
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• pp.5-13
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• 2014

Purpose - We seek feasible strategies to draw customers into a state of attitudinal/behavioral loyalty through perceived quality and perceived risk in the experienced food industry. Research design, data, and methodology - We utilize the LISREL model to examine the cause and effect relationships between customer loyalty, perceived quality, perceived risk, and three marketing proposals (brand image, store image, and promotion). We employed the quota sampling method to conduct the survey questionnaires, collecting365 effective customer samples in coffee shops/stores in Taipei City. Results - We find that store image substantially benefits consumer loyalty through perceived quality. Marketing managers can enhance store environment and atmosphere to elicit both attitudinal and behavioral aspects of customer-perceived quality and loyalty. Conclusions - This is the first paper to investigate simultaneously customer loyalty across brand image, store image, and promotion/marketing proposals in the food industry. Managers can promote brand image and store image at the same time to enhance customer-perceived quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.379-381
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• 2013

In this paper, we propose a method to detect the gradient of the spaces between sentences and to deskew in the document image. First, gradient is measured by pixels for spaces between sentences that has been done an edge extraction in document image and then skewed image is corrected by using the value of the gradient which has been measured. Since document image is divided into several areas, it shows a robust processing result by handling the margin, images, and multistage form in the document. Because the proposed method does not use pixel of the character region but use the blank area, degraded document image as well as vivid document image is effectively corrected than conventional method.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• 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 Convergence Signal Processing
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• v.17 no.1
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• pp.26-31
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• 2016

In this paper, we propose an image quality evaluation system for compact camera module and assess the effect of optical performance improvement for proposed anti-halation disk in small lens. We develop a image quality evaluation system for quality estimation of camera module image. And we also develop a program to control register in image signal processor. Finally the resolution, brightness, and color reproduction performances were evaluated image quality comparison between conventional and proposed camera module using developed quality evaluation system and ISP register control program.

• East Asian Journal of Business Economics (EAJBE)
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• v.6 no.4
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• pp.25-34
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• 2018

Purpose - The purpose of this study is to investigate the effect of Korean Wave on consumer intentions by adjusting the Korean image and image of Korean company to counterbalance the hostility through the control effect of Korean Wave. Research design and methodology - This paper measure individual and national hostility and suggest that the effect of the adjustment of Korean culture satisfaction on corporate image, product image and consumer's purchase intention. Results - This study suggests the subjects to be studied empirically by presenting research themes and models, but it is necessary to verify the model through statistical verification since it is not verified empirically. In addition, it is necessary to further control factors and identification of anti-marginal or anti-marginal products. Conclusions - This study suggests research topics that investigate how hostility affects Korean Wave in consideration of the current special situation, while conventional researches mainly focus on ethnocentrism and patriotism. In this way, this study suggests research direction that helps to enhance corporate image and product image by eliminating hostility and actively utilizing Korean Wave. The Proposal will be helpful to provide a frame for empirical analysis in future and to develop strategic means to further utilize it in international marketing.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1425-1430
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• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.891-902
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• 2012

Reversible data hiding scheme is a form of steganography in which the secret embedding data can be retrieved from a stego image for the purpose of identification, copyright protection and making a covert channel. The reversible data hiding should satisfy that not only are the distortions due to artifacts against the cover image invisible but also it has large embedding capacity as far as possible. In this paper, we propose a robust reversible data hiding scheme by exploiting the differences between a center pixel and its neighboring pixels in each sub-block of the image to embed secret data into extra space. Moreover, our scheme enhances the embedding capacity and can recover the embedded data from the stego image without causing any perceptible distortions to the cover image. Simulation results show that our proposed scheme has lower visible distortions in the stego image and provides robustness to geometrical image manipulations, such as rotation and cropping operations.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.