• 한국정보통신학회논문지
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• 제7권4호
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• pp.705-711
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• 2003

본 논문에서는 자동적으로 얼굴과 얼굴 특징점(FFPs:Facial Feature Points)을 검출하는 시스템을 제안하였다. 얼굴은 Gabor 특징에 의하여 지정된 특징점의 교점 그래프와 공간적 연결을 나타내는 에지 그래프로 표현하였으며 제안된 탄력적 특징 정합은 모델과 입력 영상에 상응하는 특징을 취하였다. 또한, 정합 모델은 국부적으로 경쟁적이고 전체적으로 협력적인 구조를 이룸으로서 영상공간에서 불규칙 확산 처리와 같은 역할을 하도록 하였으며, 복잡한 배경이나 자세의 변화, 그리고 왜곡된 얼굴 영상에서도 원활하게 동작하는 얼굴 식별 시스템을 구성함으로서 제안된 방법의 효율성을 증명하였다.

• 한국게임학회 논문지
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• 제11권3호
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• pp.63-72
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• 2011

본 논문에서는 증강 현실 환경에서 실시간 마커리스 트래킹을 수행하기 위한 특징 서술자 데이터베이스 생성 및 검색 방법을 제안한다. 먼저, 특징 서술자를 효율적으로 검색하기 위하여 특징 서술자의 형태를 기준으로 정수 부호화 하여 총 4 단계의 인덱스 데이터베이스를 구성한다. 특정 특징 서술자의 검색은 데이터베이스에서 각 단계별로 유사성 있는 후보 특징 서술자의 인덱스를 탐색하고 입력된 특징 서술자와 탐색된 모든 후보 특징 서술자들의 유클리드 거리 값 비교를 통해 이루어진다. 본 연구에서 제안한 검색방법은 형태를 기반으로 유사하지 않은 특징 서술자들을 검색 대상에서 제외하여 검색의 효율을 높였다. 제안된 방법은 기존 KD-Tree 방법에 비해서 특징 서술자당 약 16ms의 검색 속도 개선이 있었음을 확인할 수 있었다.

• 한국CDE학회논문집
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• 제14권3호
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• pp.137-149
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• 2009

For recent years, there has been significant research achievement on the feature-based multiresolution modeling technique along with widely application of three-dimensional feature-based CAD system in the areas of design, analysis, and manufacturing. The research has focused on several topics: topological frameworks for representing multiresolution solid model, criteria for the LOD, generation of valid models after rearrangement of features, and applications. This paper surveys the relevant research on these topics and suggests the future work for dissemination of this technology.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.419-423
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• 2005

This paper is a research on feature extraction and comparison method of dynamic (on-line) signature verification. We suggest desirable feature information and modified DTW(Dynamic Time Warping) and describe the performance results of our enhanced dynamic signature verification system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.284-290
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• 2013

Feature detection is very important to image processing area. In this paper we compare and analyze some characteristics of image processing algorithms for corner and blob feature detection. We also analyze the simulation results through image matching process. We show that how these algorithms work and how fast they execute. The simulation results are shown for helping us to select an algorithm or several algorithms extracting corner and blob feature.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제61차 동계학술대회논문집 28권1호
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• pp.27-28
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• 2020

본 논문에서는 초음파 영상의 통계적 특징벡터를 활용하여 폐암 분류를 제안한다. 폐암의 초음파 사진들을 비교 분석하여 각각의 label에 맞는 feature vector를 선별한다. 선택된 feature vector는 SVM을 이용하여 훈련 시킨 후, 최종적으로 폐암을 구별한다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제61차 동계학술대회논문집 28권1호
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• pp.59-60
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• 2020

본 논문에서는 특징점 추출 알고리즘 중 SIFT(Scale Invariant Feature Transform)알고리즘을 사용하여 유의미한 특징점을 추출하기 위한 방법을 제안하고자한다. 추출된 특징점을 실제 이미지에 display 해봄으로써 성능을 확인해본다.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.680-681
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• 2015

This study develops an algorithm that automatically performs reverse engineering on three-dimensional (3D) sweeping shapes using a user's pre-defined feature templates and 3D point cloud data (PCD) of sweeping shapes. Existing methods extract 3D sweeping shapes by extracting points on a PCD cross section together with the center point in order to perform curve fitting and connect the center points. However, a drawback of existing methods is the difficulty of creating a 3D sweeping shape in which the user's preferred feature center points and parameters are applied. This study extracts shape features from cross-sectional points extracted automatically from the PCD and compared with pre-defined feature templates for similarities, thereby acquiring the most similar template cross-section. Fitting the most similar template cross-section to sweeping shape modeling makes the reverse engineering process automatic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• Structural Engineering and Mechanics
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• 제63권2호
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• pp.237-249
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• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.