• 제목/요약/키워드: Approximate Euclidean Distance

검색결과 5건 처리시간 0.021초

An Approximate Euclidean Distance Calculation for Fast VQ Encoding

  • Baek, Seong-Joon;Kim, Jin-Young;Kang, Sang-Ki
    • 음성과학
    • /
    • 제11권2호
    • /
    • pp.211-216
    • /
    • 2004
  • In this paper, we present a fast encoding algorithm for vector quantization with an approximate Euclidean distance calculation. An approximation is performed by converting floating point to the near integer. An inequality between the approximate Euclidean distance and the nearest distance is developed to avoid unnecessary distance calculations. Since the proposed algorithm rejects those codewords that are impossible to be the nearest codeword, it produces the same output as conventional full search algorithm.

  • PDF

Mercer Kernel Isomap

  • 최희열;최승진
    • 한국정보과학회:학술대회논문집
    • /
    • 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (B)
    • /
    • pp.748-750
    • /
    • 2005
  • Isomap [1] is a manifold learning algorithm, which extends classical multidimensional scaling (MDS) by considering approximate geodesic distance instead of Euclidean distance. The approximate geodesic distance matrix can be interpreted as a kernel matrix, which implies that Isomap can be solved by a kernel eigenvalue problem. However, the geodesic distance kernel matrix is not guaranteed to be positive semidefinite. In this paper we employ a constant-adding method, which leads to the Mercer kernel-based Isomap algorithm. Numerical experimental results with noisy 'Swiss roll' data, confirm the validity and high performance of our kernel Isomap algorithm.

  • PDF

JADE알고리즘의 개선에 관한 연구 (A Study on the Improvement of the JADE Algorithm)

  • 윤형로;이진술;전대근;이경중
    • 대한전기학회논문지:시스템및제어부문D
    • /
    • 제52권5호
    • /
    • pp.305-310
    • /
    • 2003
  • In this paper, we proposed an IJADE(Improved joint approximate diagonalisation of eigenmatrices) which use high order statistics instead of second order statistics for data whitening. For simulation, we artificially construct signals mixed with two ECG signals, 60Hz power line interference and 16Hz sine signal and then put them into a JADE and an IJADE. To evaluate the performance of separated ECG signal in each algorithm, we have adopted indices such as kurtosis, standard deviation ratio, correlation coefficient and euclidean distance. As a results, IJ ADE showed theimproved performances as kurtosis of $2\%,$ standard deviation ratio of 0.2194, and Euclidean distance of 0.07 except correlation coefficient showing similar value. In conclusion, the proposed IJADE showed a good performance in separating ECG and a possibilities in applying to the various biological signal.

형태학적 골격에서의 거리 변환을 이용한 2차원 물체 인식 (2-D object recognition using distance transform on morphological skeleton)

  • 권준식;최종수
    • 전자공학회논문지B
    • /
    • 제33B권7호
    • /
    • pp.138-146
    • /
    • 1996
  • In this paper, w epropose a new mehtod to represent the shape and to recognize the object. The shape description and the matching is implemented by using the distance transform on the morphological skeleton. The employed distance transform is the chamfer (3,4) distance transform, because the chamfer distance transform (CDT) has an approximate value to the euclidean distance. The 2-D object can be represented by means of the distribution of the distance transform on the morphological skeleton, the number of skeletons, the sum of the CDT, and the other features are employed as the mtching parameters. The matching method has the invariant features (rotation, translation, and scaling), and then the method is used effectively for recognizing the differently-posed objects and/or marks of the different shape and size.

  • PDF

무작위 데이터 근사화를 위한 유계오차 B-스플라인 근사법 (An Error-Bounded B-spline Fitting Technique to Approximate Unorganized Data)

  • 박상근
    • 한국CDE학회논문집
    • /
    • 제17권4호
    • /
    • pp.282-293
    • /
    • 2012
  • This paper presents an error-bounded B-spline fitting technique to approximate unorganized data within a prescribed error tolerance. The proposed approach includes two main steps: leastsquares minimization and error-bounded approximation. A B-spline hypervolume is first described as a data representation model, which includes its mathematical definition and the data structure for implementation. Then we present the least-squares minimization technique for the generation of an approximate B-spline model from the given data set, which provides a unique solution to the problem: overdetermined, underdetermined, or ill-conditioned problem. We also explain an algorithm for the error-bounded approximation which recursively refines the initial base model obtained from the least-squares minimization until the Euclidean distance between the model and the given data is within the given error tolerance. The proposed approach is demonstrated with some examples to show its usefulness and a good possibility for various applications.