• 제목/요약/키워드: Linear Convolution

검색결과 125건 처리시간 0.055초

선형적 영상의 특징 추출에 관한 연구 (A Study on Feature Extraction of Linear Image)

  • 김춘영;한백룡;이대영
    • 한국통신학회논문지
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    • 제13권1호
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    • pp.74-84
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    • 1988
  • 본 논문에서는 에지검출 알고리즘을 이용하여 선형 영상에 대한 특징 추출기술을 논하였다. 에지 검출과정은 여러 개의 에지 마스크를 가지고 영상을 콘벌루션(convolution)함으로써 에지 크기와 방향을 결정하고, 이러한 에지 크기를 쓰레숄딩과 세선화하고, 간극(Gap)의 발생시 반복적으로 이 부분을 수정, 근접성(Proximity)과 방향성(orientation)에 기본을 두어 에지요소들을 연결(linking)하고, 선형근사화 시켰다. 이러한 것은 유사한 알고리즘을 연구하는 사람에게 도움이 될 것이다.

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CONVOLUTION SUMS AND THEIR RELATIONS TO EISENSTEIN SERIES

  • Kim, Daeyeoul;Kim, Aeran;Sankaranarayanan, Ayyadurai
    • 대한수학회보
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    • 제50권4호
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    • pp.1389-1413
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    • 2013
  • In this paper, we consider several convolution sums, namely, $\mathcal{A}_i(m,n;N)$ ($i=1,2,3,4$), $\mathcal{B}_j(m,n;N)$ ($j=1,2,3$), and $\mathcal{C}_k(m,n;N)$ ($k=1,2,3,{\cdots},12$), and establish certain identities involving their finite products. Then we extend these types of product convolution identities to products involving Faulhaber sums. As an application, an identity involving the Weierstrass ${\wp}$-function, its derivative and certain linear combination of Eisenstein series is established.

H.264 코덱에서 동영상 성능개선 연구 (Study on Performance Improvement of Video in the H.264 Codec)

  • 봉정식;전준현
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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    • pp.532-535
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    • 2005
  • These days, many image processing techniques have been studied for effective image compression. Among those, 2D image filtering is widely used for 2D image processing. The 2D image filtering can be implemented by performing ID linear filtering separately in the direction of horizontal and vertical. Efficiency of image compression depends on what filtering method is used. Generally, circular convolution is widely used in the 2D image filtering for image processing. However it doesn't consider correlations at the region of image boundary, therefore filtering can not be performed effectively. To solve this problem. I proposed new convolution technique using Symmetric-Mirroring convolution, satisfying the 'alias-free' and 'error-free' requirement in the reconstructed image. This method could provide more effective performance than former compression methods. Because it used very high correlative data when performed at the boundary region. In this paper, pre-processing filtering in H.264 codec was adopted to analyze efficiency of proposed filtering technique, and the simulator developed by Matlab language was used to examine the performance of the proposed method.

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표층해류 신속예측을 위한 회선적분법의 적용 (Application of a Convolution Method for the Fast Prediction of Wind-Induced Surface Current in the Yellow Sea and the East China Sea)

  • 강관수;정경태
    • 한국해안해양공학회지
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    • 제7권3호
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    • pp.265-276
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    • 1995
  • 본 논문에서는 실시간 해황예보 시스템 개발의 일환으로 수행된 회선적분법을 이용한 신속 표층유속 재현에 대하여 다룬다. 바람응력은 공간적으로 균일하고 대 기압은 무시된다. Data Base 구축을 위하여 4방향의 바람(북서, 북동, 남서, 남동)을 고려하여 각 지점의 회선적분의 가중치를 Galerkin-FEM 모형에 의해 계산하였다. 시간에 따른 바람응력이 주어지면 구성된 Data Base를 이용하여 회선적분법에 의해 신속한 예보가 가능하다. 시간적으로 변하는 임의의 바람응력은 6시간 단위로 정의되는 wind pulse의 연속으로 표현되며 총 12개의 pulse(즉, 72시간전)가 convolution product에 사용된다. 회선적분법의 적용 가능성을 확인하기 위하여 황해 이상화한 해역과 황해와 동지나해에 이르는 실제 해역에서의 수치실험이 수행되었다. 고려한 바람응력은 역풍류 생성 확인을 위하여 시간에 따라 sin 함수적으로 변하는 북풍을 고려하였고, 실험 결과 역풍류 생성의 화인과 회선적분법을 이용한 신속 표층해류 예측 가능성을 확인할 수 있었다.

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A Multi-Class Classifier of Modified Convolution Neural Network by Dynamic Hyperplane of Support Vector Machine

  • Nur Suhailayani Suhaimi;Zalinda Othman;Mohd Ridzwan Yaakub
    • International Journal of Computer Science & Network Security
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    • 제23권11호
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    • pp.21-31
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    • 2023
  • In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

SEVEN-PARAMETER MITTAG-LEFFLER OPERATOR WITH SECOND-ORDER DIFFERENTIAL SUBORDINATION RESULTS

  • Maryam K. Rasheed;Abdulrahman H. Majeed
    • Nonlinear Functional Analysis and Applications
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    • 제28권4호
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    • pp.903-917
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    • 2023
  • This paper constructs a new linear operator associated with a seven parameters Mittag-Leffler function using the convolution technique. In addition, it investigates some significant second-order differential subordination properties with considerable sandwich results concerning that operator.

컨볼루션 기법을 이용한 영역이 제한된 비정규 확률문제의 신뢰성 해석 (Reliability Analysis of the Non-normal Probability Problem for Limited Area using Convolution Technique)

  • 이현만;김태곤;최원;서교;이정재
    • 한국농공학회논문집
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    • 제55권5호
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    • pp.49-58
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    • 2013
  • Appropriate random variables and probability density functions based on statistical analysis should be defined to execute reliability analysis. Most studies have focused on only normal distributions or assumed that the variables showing non-normal characteristics follow the normal distributions. In this study, the reliability problem with non-normal probability distribution was dealt with using the convolution method in the case that the integration domains of variables are limited to a finite range. The results were compared with the traditional method (linear transformation of normal distribution) and Monte Carlo simulation method to verify that the application was in good agreement with the characteristics of probability density functions with peak shapes. However it was observed that the reproducibility was slightly reduced down in the tail parts of density function.