• 한국콘텐츠학회논문지
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• 제8권1호
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• pp.272-281
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• 2008

3D 영상에 이펙트를 표현할 수 있는 레이어 채널을 삽입함으로써 애플리케이션 렌더링에 효과적으로 이용하도록 한다. 현재의 이펙트 렌더링은 영상과 이펙트의 개별 처리 및 혼합의 방식을 사용하기 때문에 저장 공간과 영상 처리에 있어서 개별 소스를 필요로 하고 있다. 그러나 영상과 레이어 채널을 하나로 묶어 처리함으로써 비용 절약과 영상 처리 면에서 큰 효과를 볼 수 있다. 개발은 영상에 레이어 채널을 삽입하기 위해서 영상 포맷의 변경, 레이어 채널이 나타나지 않도록 숨김 기능 추가, 영상 로드시 영상과 레이어 채널을 동시 접근 가능하도록 제어, 영상과 레이어 채널이 쉽게 혼합될 수 있도록 간편한 알파 블렌딩 처리 등의 방법으로 영상 포맷을 변경하여 레이어 채널을 숨기는 기법, 일반 영상 뷰어에서도 변경된 포맷의 영상을 볼 수 있도록 개발, 레이어 채널과 영상을 같이 묶음으로써 재사용성을 높이고 모든 프로그램에 이용 가능하도록 만든다. 그러면 영상 로드시에 영상과 레이어 채널을 동시에 불러드림으로써 처리 속도 향상시키고 3D 영상에 레이어 채널을 삽입함으로써 레이어 채널 영상을 위한 소스 저장 공간을 줄일 수 있다. 또한 3D 영상과 레이어 채널의 영상을 한 번에 다룰 수 있게 되어 효과적인 이펙트 표현 가능하고 실제 애플리케이션이 될 수 있는 멀티미디어 영상 등에 효과적으로 이용이 가능할 수 있을 것으로 기대한다.

• IEIE Transactions on Smart Processing and Computing
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• 제2권3호
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• pp.130-139
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• 2013

This paper proposes a new approach for enhancing digital images based on red channel information, which has the most analogous characteristics to invisible infrared rays. Specifically, a red channel in RGB space is used to analyze the image contents and improve the visual quality of the input images but it can cause unexpected problems, such as the over-enhancement of reddish input images. To resolve this problem, inter-channel correlations between the color channels were derived, and the weighting parameters for visually pleasant image fusion were estimated. Applying the parameters resulted in significant brightness as well as improvement in the dark and bright regions. Furthermore, simple contrast and color corrections were used to maintain the original contrast level and color tone. The main advantages of the proposed algorithm are 1) it can improve a given image considerably with a simple inter-channel correlation, 2) it can obtain a similar effect of using an extra infrared image, and 3) it is faster than other algorithms compared without artifacts including halo effects. The experimental results showed that the proposed approach could produce better natural images than the existing enhancement algorithms. Therefore, the proposed scheme can be a useful tool for improving the image quality in consumer imaging devices, such as compact cameras.

• 한국멀티미디어학회논문지
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• 제24권12호
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• Korean Journal of Radiology
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• 제24권4호
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• 한국컴퓨터정보학회논문지
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• 제17권4호
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• pp.51-58
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• 2012

최근 깊이영상을 기반으로 한 3D 기술이 3D 공간감지, 3D 영상획득, 3D 인터랙션, 3D 게임 등 다양한 분야에서 응용되고 있다. 깊이영상을 생성하기 위해서는 깊이 카메라를 이용하게 되는데, 이렇게 생성된 깊이영상의 화질을 개선하기 위한 다양한 시도들이 이루어지고 있다. 본 눈문에서는 이러한 깊이 카메라 기반의 3D 응용에 있어서 깊이 영상을 개선하기 위해 영역기반의 에지 검출기를 사용하는 방법을 제안한다. 제안된 방법은 제한된 환경에서의 깊이영상을 획득하는 과정에서 발생 할 수 있는 화질열화를 후처리 또는 전처리를 통해 개선함으로써 보다 향상된 깊이 영상을 제공한다. 다양한 실험결과를 통해서 개선된 깊이영상을 객관적 화질 평가를 위해 가상시점 참조 소프트웨어에 적용하여 비교함으로써 최대 0.42dB의 화질 향상을 확인하였다. 또한 영상의 실제 시청 환경과 가장 유사한 방법인 DSCQS(Double Stimulus Continuous Quality Scale)방법을 통해서 주관적 화질의 객관적 평가를 수행함으로써 개선된 깊이영상의 효용성을 다시 확인하였다.

• 한국콘텐츠학회논문지
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• 제16권5호
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• pp.509-519
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• 2016

수요자 중심의 경쟁적 의료 환경에서는 병원이 선택되어지기 위한 전략수립을 위해 고객친화 이미지에 대한 조사와 더불어 어떠한 요인이 의료기관에 대한 고객친화 이미지에 영향을 미치는지를 파악하는 것이 마케팅 전략 수립의 기본 자료로 매우 중요한 요소이다. 본 연구에서는 지역병원들의 경영개선을 위한 효과적인 병원 마케팅 전략 수립을 위해 잠재고객과 병원직원을 대상으로 한 고객친화 이미지 평가와 고객친화 이미지에 영향을 미치는 요인들에 관한 분석을 수행하였다. 고객친화 이미지에 대한 분석결과, 지역별, 병원별 고객친화 이미지 순위와 고객친화 이미지를 구성하는 상징적, 기능적, 사회적 이미지에 대한 요소별 점수를 확인 할 수 있었는데 고객친화 이미지는 병원별보다는 각 지역별로 뚜렷한 차이를 보이고 있었다. 고객친화 이미지에 영향을 미치는 요인을 분석한 결과, 가장 큰 영향을 미치는 것은 전문성과 계속성이었으며 지명도와 경제성은 고객친화 이미지에 큰 영향을 미치지 못하는 것으로 나타났다. 본 연구의 결과는 지역병원들이 보다 전문화된 서비스를 개발하고 지역사회 내에서 안심하고 계속적인 서비스를 받을 수 있는 체계를 확립함으로 고객친화 이미지 향상을 통한 병원경영 개선을 이룰 수 있다는 점을 제시하고 있다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.185-186
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• 2006

Image Guided Surgery(IGS) system has been developed to provide exquisite and objective information to surgeons for surgical operation process. It is necessary that registration technique is important to match between 3D image model reconstructed from image modalities and the object operated by surgeon. Majority techniques of registration in IGS system have been used by recognizing fiducial markers placed on the object. However, this method has been criticized due to its invasive protocol inserting fiducial markers in patient's bone. Therefore, shape-based registration technique using geometric characteristics of the object has been invested to improve the limitation of IGS system. During Total Knee Replacement(TKR) operation, it is challenge to register with high accuracy by using shape-based registration because the area to acquire sample data from knee is limited. We have developed region-based 3D registration technique based on anatomical landmarks on the object and this registration algorithm was evaluated in femur model. It was found that region-based algorithm can improve the accuracy in 3D registration. We expect that this technique can efficiently improve the IGS system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• Smart Structures and Systems
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• 제30권3호
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• 한국간호교육학회지
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• 제7권1호
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• pp.22-37
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• 2001

This study analyzed change of nurse image after hospitalization experience. The data were collected though questionnaire survey on 87 patients, who were, for the first time, hospitalized for 5~7 days at five general hospitals in J city. The survey was performed during July 3~August 2, 2000. The nurse image was analyzed through the instrument conceived by Il-Sim Yang(1998) on the basis of four dimensions ; traditional, social, professional and personal. The collected data were processed through SPSS/WIN to examine t-test, ANOVA and paired t-test. The study results were summarized as follows ; 1. Before hospitalization, patients' score of nurse image was the highest in professional dimension, followed by personal, traditional and social in order. 2. The nurse image before hospitalization showed statistically significant differences in age(p=.009), sex(p=.027) and marital status(p=.000). 3. After hospitalization experience, the score of nurse image was the highest in personal dimension, followed by professional, traditional and social one. 4. The nurse image after hospitalization showed statistically significant differences in marital status(p=.002) only. 5. The difference of nurse image before and after hospitalization experience showed statistical significance in traditional (p=.007) and social (p=.037) dimensions. 6. The score of nurse image was improved in all dimensions after hospitalization experience. In conclusion, hospitalization experience helps improve the nurse image. Therefore, for better improvement of nurse image, it is necessary for nurses to offer their best care to hospitalized patients. In addition, efforts should be made to improve the social image of nurse, which showed lowest score.