• 전기전자학회논문지
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• 제24권4호
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• pp.942-949
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• 2020

본 논문에서는 column-parallel readout 회로에 적합하도록 개선된 CMOS 이미지 센서용 algorithmic ADC를 제안한다. 커패시터의 비율과 무관하고 연산 증폭기의 이득에 둔감하면서 증폭기 하나로 동작 할 수 있도록 기존 algorithmic ADC를 수정하고 적응형 바이어싱을 적용한 증폭기를 사용하여 높은 변환효율을 갖도록 하였다. 제안하는 ADC는 0.18-㎛ 매그나칩 CMOS 공정으로 설계되었으며, Spectre 시뮬레이션을 통해 기존 algorithmic ADC에 비해 변환속도당 전력소모가 37% 줄어 들었음을 확인하였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.237-240
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• 2007

이 논문에서는 수정된 Sequential Algorithmic Schema를 이용해서 여러 장소를 이동하면서 찍은 디지털 이미지를 효율적으로 분류할 수 있는 방법을 제안한다. 제안하는 방법은 이웃 패턴들과 특징 정보의 연속성, 유사성을 가지며 들어오는 입력 패턴에 대해 기존의 모든 군집과 유사도를 비교하는 방법이 아니라 이전 군집의 정보와 유사도를 비교하여 군집에 포함시키거나 동적으로 군집을 생성하는 효율적인 군집화 방법이다. 제안한 방법은 실험을 통해서 기존의 군집화 기법에 성능 및 속도의 효율성을 증명하였다.

• Radiation Oncology Journal
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• 제35권2호
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• pp.101-111
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• 2017

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

• 한국멀티미디어학회논문지
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• 제23권6호
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• International Journal of Control, Automation, and Systems
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• 제6권6호
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• pp.828-835
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• 2008

We present a novel method for eye location by means of a two-level classifier scheme. Locating the eye by machine-inspection of an image or video is an important problem for Computer Vision and is of particular value to applications in biomedical imaging. Our method aims to overcome the significant challenge of an eye-location that is able to maintain high accuracy by disregarding highly variable changes in the environment. A first level of computational analysis processes this image context. This is followed by object detection by means of a two-class discrimination classifier(second algorithmic level).We have tested our eye location system using FERET and BioID database. We compare the performance of two-level classifier with that of non-level classifier, and found it's better performance.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1991년도 춘계학술대회
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• pp.5-7
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• 1991

We have developed an efficient and robust image reconstruction algorithm for static impedance imaging. This improved Newton-Raphson method produced more accurate images by reducing the undesirable effects of the ill-conditioned Hessian matrix. We found that our electrical impedance tomography (EIT) system could produce two-dimensional static images from a physical phantom with 7% spatial resolution at the center and 5% at the periphery. Static EIT image reconstruction requires a large amount of computation. In order to overcome the limitations on reducing the computation time by algorithmic approaches, we implemented the improved Newton-Raphson algorithm on a parallel computer system and showed that the parallel computation could reduce the computation time from hours to minutes.

• 대한소아치과학회지
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• 제51권1호
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• pp.1-10
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• 2024

Deep learning algorithms are becoming more prevalent in dental research because they are utilized in everyday activities. However, dental researchers and clinicians find it challenging to interpret deep learning studies. This review aimed to provide an overview of the general concept of deep learning and current deep learning research in dental radiographic image analysis. In addition, the process of implementing deep learning research is described. Deep-learning-based algorithmic models perform well in classification, object detection, and segmentation tasks, making it possible to automatically diagnose oral lesions and anatomical structures. The deep learning model can enhance the decision-making process for researchers and clinicians. This review may be useful to dental researchers who are currently evaluating and assessing deep learning studies in the field of dentistry.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.125-139
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• 2023

Facial expression recognition, a topical problem in the field of computer vision and pattern recognition, is a direct means of recognizing human emotions and behaviors. This paper first summarizes the datasets commonly used for expression recognition and their associated characteristics and presents traditional machine learning algorithms and their benefits and drawbacks from three key techniques of face expression; image pre-processing, feature extraction, and expression classification. Deep learning-oriented expression recognition methods and various algorithmic framework performances are also analyzed and compared. Finally, the current barriers to facial expression recognition and potential developments are highlighted.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• 한국통신학회논문지
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• 제40권2호
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• pp.390-396
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• 2015

무선인터넷과 이동통신 기술의 발달 및 스마트폰의 급속한 확산으로 인해 사용자의 현재 및 과거의 위치 정보를 사용하여 다양한 부가정보를 제공하는 위치기반 서비스에 대한 관심이 급증하고 있다. 위치기반 서비스의 본격적인 활성화를 위해서는 정확한 측위가 기본이 된다. GPS (Global Positioning System)와 WPS (Wi-Fi Positioing System)가 상용화 되면서 측위 기술에 일대 혁신을 가져왔으나 실내환경에서 많은 제약을 가졌고 스마트폰에 일반적으로 설치되는 관성센서 (IMU: Inertia Motion Unit)를 사용한 네비게이션 (Navigation)기술을 실내 환경에서 응용하는 시도도 일정부분 성과를 거두었다. LED 조명을 이용한 실내 측위는 LED 조명으로부터 특정 신호를 수신하여 해석을 하는 Li-Fi (Light Fidelity) 통신의 부가 서비스의 한 형태로 조명 받았으나, LED 조명으로부터 신호를 수신하기 위한 수신기를 갖추어야 하는 실제적인 문제가 있다. 본 논문에서는 부가 장비 없이 스마트폰의 이미지 센서만을 이용하여 LED 조명으로부터 신호를 수신하여 해석하는 방식을 제시하고 이를 실내 측위에 이용하는 응용 서비스 기술을 제안한다.