• International journal of advanced smart convergence
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• 제4권2호
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• pp.170-176
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• 2015

The stamp is one of the crucial information of traditional historical and cultural for nations. In this paper, we purpose to detect official stamps from scanned document and recognize the Mongolian traditional, historical stamps. Therefore we performed following steps: first, we detect official stamps from scanned document based on red-color segmentation and document standard. Then we collected 234 traditional stamp images with 6 classes and 100 official stamp images from scanned document images. Also we implemented the processing algorithms for noise removing, resize and reshape etc. Finally, we proposed a new scale invariant classification algorithm based on KNN (k-nearest neighbor). In the experimental result, our proposed a method had shown proper recognition rate.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.187-188
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• 2021

본 논문에서는 실시간으로 web-cam을 이용해, 후숙과일의 불량 여부를 판단, 분류하고 불량이 없는 후숙과일의 이미지 분석을 통하여 숙성도 예측하는 시스템을 소개한다. 실시간 다중 객체인식에 탁월한 yolo모델을 활용해, 과일의 불량여부 판단 후 분류하고, 이미지를 획득한 뒤, k-mean clustering 알고리즘을 이용해, 이미지를 segmentation 한다. segmentation된 이미지에 grabcut 알고리즘의 foreground-extraction을 사용해 배경 제거를 한 뒤, cluster의 중심색상값 색상값의 면적%, 전체 면적을 이용해 현재 숙성도를 계산하고 이를 이용해 과일의 후숙 시간 데이터와 비교, 숙성이 완료될 시간을 예측한다. 기존 수작업으로 이루어지고 있는 과일의 분류작업의 인력 감소 및 정확성을 높일 수 있는 알고리즘을 제안한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.402-404
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• 2022

Recent research has shown that deep learning models are vulnerable to adversarial attacks not only in the digital but also in the physical domain. This becomes very critical for applications that have a very high safety concern, such as self-driving cars. In this study, we propose a physical adversarial attack technique for one of the common tasks in self-driving cars, namely segmentation of the urban scene. Our method can create a texture on a wall so that it can be misclassified as a road. The demonstration of the technique on a state-of-the-art cityscape pretrained model shows a fairly high success rate, which should raise awareness of more potential attacks in self-driving cars.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.134-141
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• 2023

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.187-194
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• 2024

For precise cardiac diagnostics and treatment, we introduce a novel method for patient-specific mapping between myocardial and coronary anatomy, leveraging local variations in myocardial thickness. This complex system integrates and automates multiple sophisticated components, including left ventricle segmentation, myocardium segmentation, long-axis estimation, coronary artery tracking, and advanced geodesic Voronoi distance mapping. It meticulously accounts for variations in myocardial thickness and precisely delineates the boundaries between coronary territories according to the conventional 17-segment myocardial model. Each phase of the system provides a step-by-step approach to automate coronary artery mapping onto the myocardium. This innovative method promises to transform cardiac imaging by offering highly precise, automated, and patient-specific analyses, potentially enhancing the accuracy of diagnoses and the effectiveness of therapeutic interventions for various cardiac conditions.

• 디지털융복합연구
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• 제9권1호
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• pp.189-200
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• 2011

소셜미디어(Social media) 환경이 세계적으로 '정치권력전환(power shift)'에 큰 영향을 미치고 있다. 이런 변화가 스마트한 국민의 선택(the people's choice)이다. 따라서 시대적 변화의 추세에 정치인, 정당도 시대에 걸맞는 스마트($S^6M^2AR^3T^4$)한 정치PR전략 트렌드를 구상해야 한다. 이제 정치인이나 정당은 소셜네트워크 서비스(Social Network Service, SNS)를 통하여 국민에게 모든 정책이 보다 구체적(Specific)이고, 간결(Simplicity)하게 하여, 다 같이 강한(Strong) 시너지(Synergy)의 효과를 공유(Sharing)할 수 있어야 한다. 또한 올바른 정책이 목표를 향해 힘(Mighty)있게 추진하되, 그 정책이 평가 할 수(Measurable)있도록 성취(Achievable)되어야 한다. 그리고 그 정책은 국가의 모든 분야와 관련(Relevance)된 통합적인 차원에서 국민의 신뢰(Reliability)와 현실적(Realistic)바탕에서 각 계층(Target segmentation), 국민 모두(Together)의 소외됨 없이 비전과 목표(Target)를 가지고 누구나 동등한 기회(Time)로 저 마다 행복한 삶이 추구되어야 한다. 이런 정책 바탕에서 스마트시민정당의 발전 방향과 그 대안에 대한 12가지 키워드(key words)를 제안하고 있다. 이런 스마트시민정당의 정치PR전략만이 '강한나라, 힘 있는 대한민국 건설(mighty and powerful Korea)'을 실행시킬 수 있고 국민의 선택(the people's choice)을 받게 될 것이다.

• 한국광학회지
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• 제23권5호
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• pp.222-226
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• 2012

본 논문에서는, LED 조명의 배광 특성의 조절이 용이한 반사컵 설계 방법을 제안하였다. 먼저 개별 반사컵의 구조와 높이에 대한 방사각의 변화 경향성을 파악하여, 높이에 따른 반사 특성을 확인하였다. 그 결과, 높이에 따라 반사 각도를 조절하여 설계함으로써 배광 분포의 세밀한 조절이 가능했다. 가장 간단한 2단 분할 방식의 경우 방사각의 제어나 균일도 개선을 할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제16권11호
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• pp.9-15
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• 2011

본 논문에서는 스마트폰 얼굴인식을 통해 출입을 관리하는 시스템을 설계하고 구현한다. 이를 위해 스마트폰에서 얼굴인식을 위한 사용가능한 다양한 알고리즘을 조사하였다. 얼굴 인식의 첫 단계는 얼굴검출이며 다음 단계는 얼굴인식이다. 얼굴 검출을 위해서는 컬러 세그멘테이션, 템플릿매칭 등의 알고리즘을 적용하였으며, 얼굴 인식을 위해서는 PCA(Principal Component Analysis)에 기반을 둔 Eigenface와 LDA(Linear Discriminant Analysis)에 기반을 둔 Fisherface를 비교하여 구현하고 적용하였다. 스마트 폰의 제한된 하드웨어에서 얼굴인식시스템을 구현하는 관계로 알고리즘의 정확도와 알고리즘의 계산 복잡도 사이에서 적절한 조절이 필요하였다.

• 자동차안전학회지
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• 제12권1호
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• pp.52-61
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• 2020

In recent years, the increasing interest in health-care requires the industrial products to be well-designed ergonomically. In the commercial vehicle industry, several researchers have demonstrated the driver's posture has great effect on the orthopedic desease such as fatigue, back pain, scoliosis, and so on. However, the existing sensor systems developed for measuring the driver posture in real time have suffered from inaccuracy and low reliability issues. Here, we suggest our smart vehicle seat system capable of real-time driver posture monitoring by using the air bag sensor package with high sensitivity and reliability. The ergonomic numerical model which can evaluate a driver's posture has been developed on the basis of the human body segmentation method followed by simulation-based validation. Our experimental analysis of obtained pressure distribution of a vehicle seat under the different driver's postures revealed our smart vehicle system successfully achieved the driver's real-time posture data in great agreement with our numerical model.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.