• 제목/요약/키워드: segmentation analysis

검색결과 1,175건 처리시간 0.034초

Performance Comparison Between the Envelope Peak Detection Method and the HMM Based Method for Heart Sound Segmentation

  • Jang, Hyun-Baek;Chung, Young-Joo
    • The Journal of the Acoustical Society of Korea
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    • 제28권2E호
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    • pp.72-78
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    • 2009
  • Heart sound segmentation into its components, S1, systole, S2 and diastole is the first step of analysis and the most important part in the automatic diagnosis of heart sounds. Conventionally, the Shannon energy envelope peak detection method has been popularly used due to its superior performance in locating S1 and S2. Recently, the HMM has been shown to be quite suitable in modeling the heart sound signal and its use in segmenting the heart sound signal has been suggested with some success. In this paper, we compared the two methods for heart sound segmentation using a common database. Experimental tests carried out on the 4 different types of heart sound signals showed that the segmentation accuracy relative to the manual segmentation was 97.4% in the HMM based method which was larger than 91.5% in the peak detection method.

AUTOMATIC IMAGE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING DATA BY COMBINING REGION AND EDGE INFORMATION

  • Byun, Young-Gi;Kim, Yong-II
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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    • pp.72-75
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    • 2008
  • Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

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Skin Lesion Image Segmentation Based on Adversarial Networks

  • Wang, Ning;Peng, Yanjun;Wang, Yuanhong;Wang, Meiling
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권6호
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    • pp.2826-2840
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    • 2018
  • Traditional methods based active contours or region merging are powerless in processing images with blurring border or hair occlusion. In this paper, a structure based convolutional neural networks is proposed to solve segmentation of skin lesion image. The structure mainly consists of two networks which are segmentation net and discrimination net. The segmentation net is designed based U-net that used to generate the mask of lesion, while the discrimination net is designed with only convolutional layers that used to determine whether input image is from ground truth labels or generated images. Images were obtained from "Skin Lesion Analysis Toward Melanoma Detection" challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.97, dice coefficient of 0.94 and Jaccard index of 0.89 which outperform the other existed state-of-the-art segmentation networks, including winner of ISBI 2016 challenge for skin melanoma segmentation.

인터넷 쇼핑몰 방문자의 행위 분석을 이용한 컨조인트 시장세분화 방법론에 대한 연구 (A Methodology of Conjoint Segmentation for Internet Shopping Malls Using Customer's Surfing Data)

  • Lee, Dong-Hoon;Kim, Soung-Hie
    • 한국지능정보시스템학회:학술대회논문집
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    • 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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    • pp.187-196
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    • 2000
  • A lot of Internet shopping malls strive for obtaining a competitive advantage over others in an increasingly tighter electronic marketplace. To this end, understanding customer preference toward products (or services) and administering appropriate marketing strategy is essential for their continuous survival. However, only a few marketing researchers and practicioners focused on this issue, compared with academic and industry efforts devoted to traditional market segmentation. In this paper, we suggest a methodology of conjoint segmentation for electronic shopping malls. Traditional market segmentation methodologies based on customer's profile sometimes fail to utilize abundant information given while navigating around cyber shopping malls. In this methodology, we do not impose information overload to the customer for preference elicitation, but this methodology, we do not impose information overload to the customer for preference elicitation, but capture automatically generated surfing or buying data and analyze them to get useful market segmentation information. The methodology consists of 4-stages: 1) analyzing legacy homepages, 2) data preparation, 3) estimating and interpreting the result, and 4) developing marketing mix. Our methodology was to give useful guidelines for market segmentation to companies working in the electronic marketplace.

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사업체 규모 및 출하특성 자료를 이용한 화물운송시장 분할 (Freight Market Segmentation Using Company Size and Shipment Characteristics Data)

  • 최창호;남두희
    • 대한교통학회지
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    • 제24권4호
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    • pp.103-113
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    • 2006
  • 화물운송시장의 분할은 운송시장의 효율화를 기하고 운송인이 물동량을 유치하기 위한 전략을 알기 위해 사용된다 화주 개개인의 성향을 파악하기 어려우므로 화주를 동질성을 갖는 몇 개의 군집으로 묶어 집단의 성향을 파악한다. 우리나라의 경우 표준산업분류의 배열순서에 따라 시장분할을 하여 왔다. 본 연구는 제조업체를 대상으로 새로운 운송시장 분할 가능성을 평가하였다. 연구의 결과 시장분할을 위한 적정 기준은 연간 입 출하량으로 우수한 분할능력을 보였다 또한 표준산업분류표의 배열순서에 따른 시장분할과 다른 결과를 도출하였다 본 연구는 물동량 발생 주체인 제조업체의 규모와 출하특성을 나타내는 자료를 이용하여 운송시장을 분할한 시도로서 의의가 있다.

Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning

  • Hyun Jung Koo;June-Goo Lee;Ji Yeon Ko;Gaeun Lee;Joon-Won Kang;Young-Hak Kim;Dong Hyun Yang
    • Korean Journal of Radiology
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    • 제21권6호
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    • pp.660-669
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    • 2020
  • Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

체형인식에 따른 세분화와 의복평가기준과의 관계 (Segmentation based on Perception of Somatotype and the Relation between Clothing Evaluative Criteria and Segmentation)

  • 조윤주
    • 대한가정학회지
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    • 제43권11호
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    • pp.185-196
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    • 2005
  • The purpose of this research was to determine the relation between clothing evaluative criteria and segmented groups based on the perception of somatotype. The data for this research were collected from questionnaires of 192 females in Busan. Data were analyzed by frequency, factor analysis, cluster analysis, discriminant analysis, and regression analysis. Cluster analysis was used to identify groups of respondents based on the perception of somatotype difference factors. Based on the findings, three distinct groups were clustered: thin, moderate, fat. There were significant differences among the three groups in terms of clothing evaluative criteria. The result of regression analysis revealed that the perception of somatotype is a major determinant to influence the clothing evaluative criteria. The thin group preferred practical clothes while the fat group liked symbol clothes.

효율적인 영어 구문 분석을 위한 최대 엔트로피 모델에 의한 문장 분할 (Intra-Sentence Segmentation using Maximum Entropy Model for Efficient Parsing of English Sentences)

  • 김성동
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제32권5호
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    • pp.385-395
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    • 2005
  • 긴 문장 분석은 높은 분석 복잡도로 인해 기계 번역에서 매우 어려운 문제이다. 구문 분석의 복잡도를 줄이기 위하여 문장 분할 방법이 제안되었으며 본 논문에서는 문장 분할의 적용률과 정확도를 높이기 위한 최대 엔트로피 확률 모델 기반의 문장 분할 방법을 제시한다. 분할 위치의 어휘 문맥적 특징을 추출하여 후보 분할 위치를 선정하는 규칙을 학습을 통해 자동적으로 획득하고 각 후보 분할 위치에 분할 확률 값을 제공하는 확률 모델을 생성한다. 어휘 문맥은 문장 분할 위치가 표시된 말뭉치로부터 추출되며 최대 엔트로피 원리에 기반하여 확률 모델에 결합된다. Wall Street Journal의 문장을 추출하여 학습 데이타를 생성하는 말뭉치를 구축하고 네 개의 서로 다른 영역으로부터 문장을 추출하여 문장 분할 실험을 하였다. 실험을 통해 약 $88\%$의 문장 분할의 정확도와 약 $98\%$의 적용률을 보였다. 또한 문장 분할이 효율적인 파싱에 기여하는 정도를 측정하여 분석 시간 면에서 약 4.8배, 공간 면에서 약 3.6배의 분석 효율이 향상되었음을 확인하였다.

딥러닝 기반의 Semantic Segmentation을 위한 Residual U-Net에 관한 연구 (A Study on Residual U-Net for Semantic Segmentation based on Deep Learning)

  • 신석용;이상훈;한현호
    • 디지털융복합연구
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    • 제19권6호
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    • pp.251-258
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    • 2021
  • 본 논문에서는 U-Net 기반의 semantic segmentation 방법에서 정확도를 향상시키기 위해 residual learning을 활용한 인코더-디코더 구조의 모델을 제안하였다. U-Net은 딥러닝 기반의 semantic segmentation 방법이며 자율주행 자동차, 의료 영상 분석과 같은 응용 분야에서 주로 사용된다. 기존 U-Net은 인코더의 얕은 구조로 인해 특징 압축 과정에서 손실이 발생한다. 특징 손실은 객체의 클래스 분류에 필요한 context 정보 부족을 초래하고 segmentation 정확도를 감소시키는 문제가 있다. 이를 개선하기 위해 제안하는 방법은 기존 U-Net에 특징 손실과 기울기 소실 문제를 방지하는데 효과적인 residual learning을 활용한 인코더를 통해 context 정보를 효율적으로 추출하였다. 또한, 인코더에서 down-sampling 연산을 줄여 특징맵에 포함된 공간 정보의 손실을 개선하였다. 제안하는 방법은 Cityscapes 데이터셋 실험에서 기존 U-Net 방법에 비해 segmentation 결과가 약 12% 향상되었다.

Best Combination of Binarization Methods for License Plate Character Segmentation

  • Yoon, Youngwoo;Ban, Kyu-Dae;Yoon, Hosub;Lee, Jaeyeon;Kim, Jaehong
    • ETRI Journal
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    • 제35권3호
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    • pp.491-500
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    • 2013
  • A connected component analysis from a binary image is a popular character segmentation method but occasionally fails to segment the characters owing to image noise and uneven illumination. A multimethod binarization scheme that incorporates two or more binary images is a novel solution, but selection of binarization methods has never been analyzed before. This paper reveals the best combination of binarization methods and parameters and presents an in-depth analysis of the multimethod binarization scheme for better character segmentation. We carry out an extensive quantitative evaluation, which shows a significant improvement over conventional single-method binarization methods. Experiment results of six binarization methods and their combinations with different test images are presented.