• 제목/요약/키워드: Wavelet features

검색결과 387건 처리시간 0.022초

움직임 각도의 주파수 분석을 통한 활동성 분석 (Fish's Activity Analysis through Frequency Analysis of Angle Information)

  • 김철기
    • 한국콘텐츠학회논문지
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    • 제7권5호
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    • pp.10-15
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    • 2007
  • 본 논문에서는 기존의 컴퓨터비전 기술을 이용한 생물체의 자동 추적 시스템을 통하여 얻어진 움직임 데이터를 이용하여 일반적인 움직임의 범위를 벗어나는 비정상적인 움직임 제적을 탐지하여 주는 방법을 제안하고 있다. 또한 웨이블릿 변환을 통한 부대역간 주파수 성분의 특성을 이용하여 움직임 궤적의 분석에 적용하는 방법을 제시하고 있다. 실험 결과를 통하여 인위적인 약물 처리 전 후에 탐지되는 활동성을 표현하는 특징점의 수가 통계학적으로 유의함을 확인하였다.

웨이브릿 특징과 신경망을 이용한 지형분류 (Terrain Cover Classification Using Wavelet Features and Neural Networks)

  • 성기열;곽동민;김도종;유준
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2008년도 하계종합학술대회
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    • pp.853-854
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    • 2008
  • The terrain perception technology using passive sensors plays a key role to enhance autonomous mobility for UGV. We present an effective method to classify terrain covers based on the color information. Considering a real-time implementation, neural network is applied for the terrain classifier and wavelet features extracted from the images are used. Test results show that the proposed algorithm has a promising classification performance.

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Wavelet based Feature Extraction of Human Face

  • Kim, Yoon-ho;Lee, Myung-kil;Ryu, Kwang-ryol
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 2001년도 춘계종합학술대회
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    • pp.656-659
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    • 2001
  • Human have a notable ability to recognize faces, which is one of the most common visual feature in our environment. In regarding face pattern, just like other natural object, a geometrical interpretation of face is difficult to achieve. In this paper, we present wavelet based approach to extract the face features. Proposed approach is similar to the feature based scheme, where the feature is derived from the intensity data without detecting any knowledge of the significant feature. Topological graphs are involved to represent some relations between facial features. In our experiments, proposed approach is less sensitive to the intensity variation.

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Wavelet based Feature Extraction of Human face

  • Kim, Yoon-Ho;Lee, Myung-Kil;Ryu, Kwang-Ryol
    • 한국정보통신학회논문지
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    • 제5권2호
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    • pp.349-355
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    • 2001
  • Human have a notable ability to recognize faces, which is one of the most common visual feature in our environment. In regarding face pattern, just like other natural object, a geometrical interpretation of face is difficult to achieve. In this paper, we present wavelet based approach to extract the face features. Proposed approach is similar to the feature based scheme, where the feature is derived from the intensity data without detecting any knowledge of the significant feature. Topological graphs are involved to represent some relations between facial features. In our experiments, proposed approach is less sensitive to the intensity variation.

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동시 발생 행렬의 특성함수 모멘트를 이용한 접합 영상 검출 (Spliced Image Detection Using Characteristic Function Moments of Co-occurrence Matrix)

  • 박태희;문용호;엄일규
    • 대한임베디드공학회논문지
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    • 제10권5호
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    • pp.265-272
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    • 2015
  • This paper presents an improved feature extraction method to achieve a good performance in the detection of splicing forged images. Strong edges caused by the image splicing destroy the statistical dependencies between parent and child subbands in the wavelet domain. We analyze the co-occurrence probability matrix of parent and child subbands in the wavelet domain, and calculate the statistical moments from two-dimensional characteristic function of the co-occurrence matrix. The extracted features are used as the input of SVM classifier. Experimental results show that the proposed method obtains a good performance with a small number of features compared to the existing methods.

Visual Feature Extraction Technique for Content-Based Image Retrieval

  • Park, Won-Bae;Song, Young-Jun;Kwon, Heak-Bong;Ahn, Jae-Hyeong
    • 한국멀티미디어학회논문지
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    • 제7권12호
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    • pp.1671-1679
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    • 2004
  • This study has proposed visual-feature extraction methods for each band in wavelet domain with both spatial frequency features and multi resolution features. In addition, it has brought forward similarity measurement method using fuzzy theory and new color feature expression method taking advantage of the frequency of the same color after color quantization for reducing quantization error, a disadvantage of the existing color histogram intersection method. Experiments are performed on a database containing 1,000 color images. The proposed method gives better performance than the conventional method in both objective and subjective performance evaluation.

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Support Vector Machine Based Arrhythmia Classification Using Reduced Features

  • Song, Mi-Hye;Lee, Jeon;Cho, Sung-Pil;Lee, Kyoung-Joung;Yoo, Sun-Kook
    • International Journal of Control, Automation, and Systems
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    • 제3권4호
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    • pp.571-579
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    • 2005
  • In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

Implementation Strategy for the Numerical Efficiency Improvement of the Multiscale Interpolation Wavelet-Galerkin Method

  • Seo Jeong Hun;Earmme Taemin;Jang Gang-Won;Kim Yoon Young
    • Journal of Mechanical Science and Technology
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    • 제20권1호
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    • pp.110-124
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    • 2006
  • The multi scale wavelet-Galerkin method implemented in an adaptive manner has an advantage of obtaining accurate solutions with a substantially reduced number of interpolation points. The method is becoming popular, but its numerical efficiency still needs improvement. The objectives of this investigation are to present a new numerical scheme to improve the performance of the multi scale adaptive wavelet-Galerkin method and to give detailed implementation procedure. Specifically, the subdomain technique suitable for multiscale methods is developed and implemented. When the standard wavelet-Galerkin method is implemented without domain subdivision, the interaction between very long scale wavelets and very short scale wavelets leads to a poorly-sparse system matrix, which considerably worsens numerical efficiency for large-sized problems. The performance of the developed strategy is checked in terms of numerical costs such as the CPU time and memory size. Since the detailed implementation procedure including preprocessing and stiffness matrix construction is given, researchers having experiences in standard finite element implementation may be able to extend the multi scale method further or utilize some features of the multiscale method in their own applications.

수정된 웨이블릿 변환 개념을 이용한 계측 가속도 기록의 이중 적분법 (Double Integration of Measured Acceleration Record using the Concept of Modified Wavelet Transform)

  • 이형진;박정식
    • 한국지진공학회논문집
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    • 제7권5호
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    • pp.11-17
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    • 2003
  • 일반적으로 토목구조물에서 계측된 가속도 신호의 대부분은 적분이 매우 힘들다. 그 이유는 토목구조에서 계측된 가속도 신호는 일반적으로 비정상신호이며 또한 비가우시안 노이즈와 저주파 노이즈를 포함하고 있어, 저주파 성분이 증폭되는 적분과정에서 수치적 불안정성이 발생할 수 있기 때문이다. 따라서, 본 논문에서는 비정상 신호처리에 탁월한 웨이블릿 변환의 개념을 비가우시안 노이즈와 저주파 노이즈에 대해 확장한 수정된 웨이블릿 변환을 이용한 가속도 기록의 이중 적분방법을 제시하였다. 또한, 예제해석을 통해 제시된 방법이 비정상 신호의 노이즈 및 비가우시안 노이즈와 저주파 노이즈를 제거에 우수한 성능을 보이고 있음을 보였다.

Speckle Noise Reduction and Edge Enhancement in Ultrasound Images Based on Wavelet Transform

  • Kim, Yong-Sun;Ra, Jong-Beom
    • 대한의용생체공학회:의공학회지
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    • 제29권2호
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    • pp.122-131
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    • 2008
  • For B-mode ultrasound images, we propose an image enhancement algorithm based on a multi-resolution approach, which consists of edge enhancing and noise reducing procedures. Edge enhancement processing is applied sequentially to coarse-to-fine resolution images obtained from wavelet-transformed data. In each resolution, the structural features of each pixel are examined through eigen analysis. Then, if a pixel belongs to an edge region, we perform two-step filtering: that is, directional smoothing is conducted along the tangential direction of the edge to improve continuity and directional sharpening is conducted along the normal direction to enhance the contrast. In addition, speckle noise is alleviated by proper attenuation of the wavelet coefficients of the homogeneous regions at each band. This region-based speckle-reduction scheme is differentiated from other methods that are based on the magnitude statistics of the wavelet coefficients. The proposed algorithm enhances edges regardless of changes in the resolution of an image, and the algorithm efficiently reduces speckle noise without affecting the sharpness of the edge. Hence, compared with existing algorithms, the proposed algorithm considerably improves the subjective image quality without providing any noticeable artifacts.