• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.203-210
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• 1996

A linear neural unit with a modified anti-Hebbian learning rule has been shown to be able to optimally fit curves, surfaces, and hypersurfaces by adaptively extracting the minor component of the input data set. In this paper, we study how to use the robust version of this neural fitting method for linear regression analysis. Furthermore, we compare this method with other methods when data set is contaminated by outliers.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.41-44
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• 2000

With the advent of DNA microarray and "chip" technologies, gene expression in an organism can be monitored on a genomic scale, allowing the transcription levels of many genes to be measured simultaneously. Functional interpretation of massive expression data and linking such data to DNA sequences have become the new challenges to bioinformatics. I will us yeast cell cycle expression data analysis as an example to demonstrate how special database and computational methods may be used for extracting functional information, I will also briefly describe a novel clustering algorithm which has been applied to the cell cycle data.

• 한국약용작물학회:학술대회논문집
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• 한국약용작물학회 2009년도 심포지엄 및 춘계학술발표회
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• pp.329-330
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• 2009

• 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.

• International Journal of Contents
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• 제6권2호
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• pp.1-5
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• 2010

The rapid growth in communication technology has led to the development of effective ways of sharing ideas and information in the form of speech and images. Understanding this information has become an important research issue and drawn the attention of many researchers. Text in a digital image contains much important information regarding the scene. Detecting and extracting this text is a difficult task and has many challenging issues. The main challenges in extracting text from natural scene images are the variation in the font size, alignment of text, font colors, illumination changes, and reflections in the images. In this paper, we propose a connected component based method to automatically detect the text region in natural images. Since text regions in mages contain mostly repetitions of vertical strokes, we try to find a pattern of closely packed vertical edges. Once the group of edges is found, the neighboring vertical edges are connected to each other. Connected regions whose geometric features lie outside of the valid specifications are considered as outliers and eliminated. The proposed method is more effective than the existing methods for slanted or curved characters. The experimental results are given for the validation of our approach.

• Journal of information and communication convergence engineering
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• 제12권4호
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권3호
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• pp.276-292
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• 2004

본 논문에서는 폐 질환 진단에 필요한 EBT(Electron Beam Tomography) 흉부 영상에서 폐 영역을 추출하고, 추출된 폐 영역에서 폐엽의 경계(pulmonary fissure)를 찾아 폐엽(lobe) 단위로 분할하는 방법을 제안하였다. EBT 흉부 영상을 분석하여 히스토그램을 기반으로 하는 임계치 방법과, 수학적형태학을 적용하여 폐 영역을 추출하였고 본 논문에서 제안한 adaptive filter scale을 사용한 에지 연산자와 폐엽 경계(pulmonary fissure)에 대한 해부학적 지식을 바탕으로 폐 영역을 폐엽 단위로 분할하였다. 본 논문에서 제안한 방법을 총 102개의 영상에 대해 실험한 결과는 폐 영역 추출에서 95% 이상의 정확도를 보여주었고 폐엽 경계선 추출에서 5 픽셀 이하의 거리오차를 나타내었다.

• 한국멀티미디어학회논문지
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• 제2권2호
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• pp.129-136
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• 1999

특정 이미지에서의 선형적 특정은 이미지를 분석하고 이해하는데 충분한 정보를 제공하기도 한다. 본고에 서는 이미지에서 선형적 특징을 추출하기 위한 신뢰성 있는 방법을 제시한다. 일반적으로 후프 변형 방법은 이러한 선형적 특정을 추출하는 최적의 방법 중의 하나로 인식되어 왔다. 대부분의 후프 기반 방법들은 특정 edge 모델올 선택하고, 인식된 edge 픽셀의 속성을 반영하는 변형식을 활용하여 파라미터 공간에 그 발생빈도 를 기록하는 과정을 거치게 된다. 주로 edge 픽셀의 gradient 크기와 방향이 선형적 특정을 결정하는데 사용되 지만, 본고에서는 그 값틀이 퍼지변수로 활용될 수 있음을 보이고 파라미터 공간에 누적값을 계산하는데 활용한다- 이 방법을 기존의 방법과 비교하기 위하여 에러 측정 방식을 제안하고, 실험을 한 결과, 기존의 방법과 비교하여 우수한 성능을 보인다.

• 디지털산업정보학회논문지
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• 제13권4호
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• 전자통신동향분석
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• 제35권5호
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• pp.112-122
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• 2020

In 3D computer graphics, a depth map is an image that provides information related to the distance from the viewpoint to the subject's surface. Stereo sensors, depth cameras, and imaging systems using an active illumination system and a time-resolved detector can perform accurate depth measurements with their own light sources. The 3D image information obtained through the depth map is useful in 3D modeling, autonomous vehicle navigation, object recognition and remote gesture detection, resolution-enhanced medical images, aviation and defense technology, and robotics. In addition, the depth map information is important data used for extracting and restoring multi-view images, and extracting phase information required for digital hologram synthesis. This study is oriented toward a recent research trend in deep learning-based 3D data analysis methods and depth map information extraction technology using a convolutional neural network. Further, the study focuses on 3D image processing technology related to digital hologram and multi-view image extraction/reconstruction, which are becoming more popular as the computing power of hardware rapidly increases.