• 대한조선학회논문집
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• 제56권6호
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• pp.473-479
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• 2019

Welding is one of the most popular joining methods and most welding quality estimation methods are executed using joined material. This paper propose welding quality estimation methods using dynamic current, voltage and resistance which are obtained during welding in real time. There are many kinds of welding method. Among them, we focused on the projection welding and gathered dynamic characteristics from two different types of projection welding. For image learning, graphs are drawn using obtained current, voltage and resistance, and the graphs are converted to images. The images are labeled with two sub-categories - normal and defect. For deep learning of images obtained from welding, Convolutional Neural Network (CNN) is applied, and Tensorflow was used as a framework for deep learning. With two resistance welding test datasets, we conclude that the Convolutional Neural Network helps in predicting the welding quality.

• 전기학회논문지
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• 제65권8호
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• pp.1407-1417
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• 2016

In this paper, we propose a multi-level content transmission mechanism for intelligent quality of service (QoS) in social networking services (SNSs). Because existing SNSs and related work send image content to a client with a single fixed mechanism, they cannot consistently support content accessibility according to different conditions of QoS factors such as network congestion and throughput. In the proposed image transmission mechanism, our communication middleware (CM) provides an SNS developer with three transmission modes so that an SNS server or client can dynamically change the quality of images if required. In each transmission mode, an SNS server can send images to a requesting client with original high quality, thumbnail quality, or send only text information. With varying qualities of downloaded images, an SNS developed on top of CM can provide users with consistent QoS for access to SNS content.

• 융합신호처리학회논문지
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• 제11권4호
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• pp.278-288
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• 2010

본 논문에서는 ITU-R 기고문과 권고안에 따라 양안 업체 영상에 대한 화질, 깊이감, 명료성, 실재감 항목을 모노 영상에 대비하여 DSCQS(Double-Stimuli Continuous Quality Scale) 방법을 사용하여 평정척도법에 의해 분석하였다. 평가 결과는 자연 실외 영상, 그래픽 영상, 실내 영상으로 구성된 평가 대상 영상에 대하여 전반적인 화질의 차나 명료성은 모노인 경우나 입체 영상인 경우나 별 차이를 나타내지 않았으나(대강 3.0 이상 -4.0 이하), 입체 영상의 깊이감 인지와 실재감에 대해서는 업체 영상인 경우 모두 5.0등급 중 4.0 등급 이상을 나타내어 깊이감 및 실재감 정보가 주는 인상이 매우 큼을 시사해준다. 양안 3DTV 촬영이나 편집시 시차 등 휴먼 팩터로 평가결과가 고려되어져야 할 것이다.

• 한국가정과학회지
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• 제8권1호
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• pp.5-18
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• 2005

The purposes of this study were to identify brand image and advertising image perception maps of domestic make-up products. A self-administered questionnaire was used for data collection. KYST, CORAN and SPSS PC(Ver. 12.0) were used for data analysis. The results were as follows: 1. Brand images of Etude, Isa Knox, and Laneige were perceived as unique, stimulative, high quality, elegant, modern, and sophisticated. Brand image of Cathycat was perceived highly in high quality, elegant, modern, and sophisticated, but low in unique and stimulative. Brand image of Lac Vert was perceived high in unique and stimulative, but low in high quality, elegant, modern, and sophisticated. Brand images of Hercyna and Vov were the lowest. 2. Advertisement images of Etude was perceived as modern, sophisticated, familiar, and unique, but Lac Vert was perceived adversely, Advertising images of Laneigne and Isa Knox were high in modern, sophisticated and familiar, but low in uniqueness. And advertising images of Hercyna, Cathycat, and Vov were perceived as modern, sophisticated, and familiar.

• 대한전자공학회논문지
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• 제25권6호
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• pp.663-671
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• 1988

In this paper, in order to reduce the blocking effect of block coded images, we propose the method considering the lowpass and bandpass components of Granrath's human visual model. This method consists of two-stage enhancement procedure. The first step is lowpass filtering which smooths out the blocking effect, and the second step is a high frequency enhancement procedure to increase the contrast decreased by the lowpass filtering in the first step. In the first step, the one-dimensional Gaussian filter which aligthns parallel to the edge direction is considered to preserve the edge in the block and the two-dimensional Gaussian filter is used to smooth out the blocking effect near the block boundaries. In the second step, the lowpass and bandpass components of the Granrath's model are considered to increase contrast in a restored image. The performance comparison of the proposed method and the existing mehtods is made by a computer simulation with several block coded images. We can see that the enhancement in the subjective quality of images of the proposed method is more significant than the enhancement in the subjective quality of images of the proposed method is more significant than the existing methods, though the proposed method does not show better performance on the PSNR gain, the poor measure of picture quality for block coded images.

• 치과방사선
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• 제23권1호
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• pp.125-136
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• 1993

The geometrically standardized intraoral radiographs using 5 occlusal registration materials were taken serially from immediate, 1 day, 2, 4, 8, 12, and 16 weeks after making the bite blocks. And the resultant images were digitally subtracted using the immediately taken film as reference images. The qualities of those subtracted images were evaluated to check the degree of reproducibility of each impression material. The results were as follows: 1. The standard deviations of the grey scales of the overall subtracted images were 4.9 for Exal1ex, 7.2 for Pattern resin, 9.0 for Tooth Shade Acrylic, 12.2 for XCP only, 14.8 for Impregum. the lesser the standard deviation, the better the quality of the subtracted images. 2. The standard deviation of the grey scales of the overall subtracted images were grossly related to those of the localized horizontal line of interest. 3. Exaflex which showed the best subtracted image quality had 15 cases of straight, 14 cases of wave, 1 case of canyon shape. Impregum which showed the worst subtracted image quality had 4 cases of straight, 8 cases of wave, 18 cases of canyon shape respectively.

• 스마트미디어저널
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• 제6권3호
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• pp.57-63
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• 2017

차량번호인식 개선을 위해서는 무엇보다 양질의 차량이미지를 획득하는 것이 무엇보다 먼저 선행되어야 하는 필수적인 요소이다. 일반적인 도로영상들은 시간, 햇빛, 날씨 등 다양한 환경의 영향을 받아 번호판 밝기가 일률적이지 않고 다양한 형태로 나타나기 때문에 여러 가지 이미지 보정 기능을 거치게 되고 이로 인하여 인식속도 저하, 인식률 저하 등이 나타난다. 따라서, 본 논문에서는 실시간 영상 촬영 시 번호판 주위의 밝기를 측정하여 카메라의 shutter, bright, gain등 이미지 밝기와 품질에 영향을 주는 각 요소를 실시간으로 제어하여 빠르고 선명한 고품질의 차량 이미지 촬영하기 위해 실시간 도로 영상을 통하여 제안된 방법을 테스트 하였다.

• 디지털산업정보학회논문지
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• 제18권4호
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• pp.1-11
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• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.180-187
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• 2021

In this paper, we present a computational volumetric reconstruction method for three-dimensional (3D) photon counting imaging with enhanced visual quality when low-resolution elemental images are used under photon-starved conditions. In conventional photon counting imaging with low-resolution elemental images, it may be difficult to estimate the 3D scene correctly because of a lack of scene information. In addition, the reconstructed 3D images may be blurred because volumetric computational reconstruction has an averaging effect. In contrast, with our method, the pixels of the elemental image rearrangement technique and a Bayesian approach are used as the reconstruction and estimation methods, respectively. Therefore, our method can enhance the visual quality and estimation accuracy of the reconstructed 3D images because it does not have an averaging effect and uses prior information about the 3D scene. To validate our technique, we performed optical experiments and demonstrated the reconstruction results.