• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.409-415
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• 2022

In computer vision, an image of a measurement target is acquired using a camera. And feature values, vectors, and regions are detected by applying algorithms and library functions. The detected data is calculated and analyzed in various forms depending on the purpose of use. Computer vision is being used in various places, especially in the field of automatically recognizing automobile parts or measuring the quality. Computer vision is being used as the term machine vision in the industrial field, and it is connected with artificial intelligence to judge product quality or predict results. In this study, a vision system for judging the quality of automobile parts was built, and the results were compared by applying five machine learning classification models to the produced data.

• Annual Conference of KIPS
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• 2023.11a
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• pp.475-478
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• 2023

컴퓨터 단층촬영(CT)을 활용한 골격근 단면적은 근감소증과 관련된 기능을 평가하는 데 사용된다. 일반적인 근감소증 연구는 요추 3번의 골격근량을 주로 보지만 암 또는 폐절제술과의 상관관계를 예측하기 위한 다양한 연구에서는 흉추 4번, 7번, 8번, 10번, 12번 다양한 수준의 골격근량으로 연구를 진행하고 있음을 알 수 있다. 본 논문에서는 흉부와 복부 CT 영상에서 근감소증 진단을 위해서 흉추와 요추의 영역별 슬라이스를 검출하기 위해서 CNN 구조의 EfficientNetV2를 전이학습하여 인공지능 모듈을 개발하였다. 인공지능 모듈은 전체 흉부 및 복부 CT 영상에서 Cervical, T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, L1, L2, L3, L4, L5, Sacral 총 19 클래스를 검출하도록 하였다. Test 데이터셋을 사용하여 Confusion Matrix와 Grad-CAM으로 모델의 정확도를 시각화하여 보였으며 검증으로 인공지능 모듈의 정확성을 측정하였다. 끝으로 우리가 개발한 다기관 공동연구 지원플랫폼에 적용하여 시각화된 결과를 보였다.

• Journal of Platform Technology
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• v.12 no.1
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• pp.128-140
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• 2024

'Deepfake' refers to a video synthesis technique that utilizes various artificial intelligence technologies to create highly realistic fake content, causing serious confusion to individuals and society by being used for generating fake news, fraud, malicious impersonation, and more. To address this issue, there is a need for methods to detect malicious images generated by deepfake accurately. In this paper, we extract and analyze saliency features from deepfake and real images, and detect candidate synthesis regions on the images, and finally construct an automatic deepfake detection model by focusing on the extracted features. The proposed saliency feature-based model can be universally applied in situations where deepfake detection is required, such as synthesized images and videos. To demonstrate the performance of our approach, we conducted several experiments that have shown the effectiveness of the deepfake detection task.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.87-98
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• 2012

Understanding agent's intent is an essential component of the human-computer interaction of ubiquitous computing. Because correct inference of subject's intention in ubiquitous computing system helps particularly to understand situations that involve collaboration among multiple agents or detection of situations that can pose a particular activity. This paper, inspired by people have a mechanism for interpreting one another's actions and for inferring the intentions and goals that underlie action, proposes an approach that allows a computing system to quickly recognize the intent of agents based on experience data acquired through prior capabilities of activities recognition. To proceed intention recognition, proposed method uses formulations of Hidden Markov Models (HMM) to model a system's prior experience and agents' action change, then makes for system infer intents in advance before the agent's actions are finalized while taking the perspective of the agent whose intent should be recognized. Quantitative validation of experimental results, while presenting an accurate rate, an early detection rate and a correct duration rate with detecting the intent of several people performing various activities, shows that proposed research contributes to implement effective intent recognition system.

• Journal of Astronomy and Space Sciences
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• v.26 no.4
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• pp.511-520
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• 2009

In this study, the Ocean Tide Loading (OTL) constituents were detected by the Precise Point Positioning (PPP) technique using GPS. Then, the GPS estimates of OTL constituents were compared with the predictions of the ocean tide models. We picked three permanent GPS stations as test sites and they are ICNW, SEOS, and CJUN. To detect the OTL constituents using GPS, we created vertical coordinate time series at 10-minute intervals using the PPP approach implemented in the GIPSY software. Through the tidal harmonic analysis of this height time series, the four major constituents ($M_2$, $S_2$, $K_1$, $O_1$) were determined. The amplitude obtained from the GPS height time series of the OTL constituents showed best match with the model predictions at CJUN, while the phase showed closest match at ICNW. The amplitude accuracy of the $M_2$, which is the dominant factor out of the 11 major constituents, was 24.8% on average.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.838-846
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• 2004

In the case where both a camera and a target are moving at the same time, the image background is successively changed, and the overlap with other moving objects is apt to be generated. The snake algorithms have been variously used in tracking the object, but it is difficult to be applied in the excessive overlap with other objects and the large bias between the snake and the target. To solve this problem, this paper presents an extended snake model. It includes an additional energy function which considers the temporal variation rate of the snake's area and a SSD algorithm which generates the template adaptive to the snake detected in the previous frame. The new energy function prevents the snake from over-shrinking or stretching and the SSD algorithm with adaptively changing template allows the prediction of the target's position in the next frame. The experimental results have shown that the proposed algorithm successfully tracks the target even when the target is temporarily occluded by other objects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2492-2498
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• 2013

In this paper, A lane tracking algoritm is proposed for lane departure warning system. To eliminate perspective effect, input image is converted into Bird's View by inverse perspective mapping. Next, suitable features are extracted for lane detection. Using clustering and lane similarity function with noise suppression features are extracted. Finally, lane model is calculated using RANSAC and lane model is tracked using Kalman Filter. Experimental results show that the proposed algorithm can be processed within 20ms and its detection rate approximately 90% on the highway in a variety of environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.175-182
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• 2010

The paper presents a novel algorithm for face and iris detection with the application for driver iris monitoring. The proposed algorithm consists of the following major steps: Skin-color segmentation, facial features segmentation, and iris positioning. For the skin-segmentation we applied a multi-layer perceptron to approximate the statistical probability of certain skin-colors, and filter out those with low probabilities. The next step segments the face region into the following categories: eye, mouth, eye brow, and remaining facial regions. For this purpose we propose a novel segmentation technique based on estimation of facial class probability density functions (PDF). Each facial class PDF is estimated on the basis of salient features extracted from a corresponding facial image region. Then pixels are classified according to the highest probability selected from four estimated PDFs. The final step applies the circular Hough transform to the detected eye regions to extract the position and radius of the iris. We tested our system on two data sets. The first one is obtained from the Web and contains faces under different illuminations. The second dataset was collected by us. It contains images obtained from video sequences recorded by a CCD camera while a driver was driving a car. The experimental results are presented, showing high detection rates.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.155-167
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• 2007

There are four types of human beings according to the Sasang Typology, Oriental medical doctors frequently prescribe healthcare information and treatment depending on one's type, The feature ratios (Table 1) on the human face are the most important criterions to decide which type a patient is. In this paper, we proposed a system to extract these feature ratios from the people's side face, There are two challenges in acquiring the feature ratio: one that selecting representative features; the other, that detecting region of interest from human profile facial image effectively and calculating the feature ratio accurately. In our system, an adaptive color model is used to separate human side face from background, and the method based on geometrical model is designed for region of interest detection. Then we present the error analysis caused by image variation in terms of image size and head pose, To verify the efficiency of the system proposed in this paper, several experiments are conducted using about 173 korean's left side facial photographs. Experiment results shows that the accuracy of our system is increased 17,99% after we combine the front face features with the side face features, instead of using the front face features only.

• The Journal of the Acoustical Society of Korea
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• v.21 no.7
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• pp.632-640
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• 2002

In this paper, we proposed ACM (Anti-filler confidence measure) to compensate shortcoming of conventional RLJ-CM (RLJ-CM) and NCM (normalized CM), and integrated proposed ACM and conventional NCM using HCM (hybrid CM). Proposed ACM analyzes that FA (false acceptance) happens by the construction method of anti-phone model, and presumed phoneme sequence in actuality using phoneme recognizer to compensate this. We defined this as anti-phone model and used in confidence measure calculation. Analyzing feature of two confidences measure, conventional NCM shows good performance to FR (false rejection) and proposed ACM shows good performance in FA. This shows that feature of each other are complementary. Use these feature, we integrated two confidence measures using weighting vector α And defined this as HCM. In MDR (missed detection rate) 10% neighborhood, HCM is 0.219 FA/KW/HR (false alarm/keyword/hour). This is that Performance improves 22% than used conventional NCM individually.