• 제목/요약/키워드: gaussian weight

검색결과 113건 처리시간 0.022초

기하학적 특징 모델을 이용한 강건한 영상 모자이크 기법 (Robust Image Mosaic using Geometrical Feature Model)

  • 김정훈;김대현;윤용인;최종수
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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    • pp.13-16
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    • 2000
  • This paper presents a robust method to combine a collection of images with small fields of view to obtain an image with a large field of view. In the previous works, there are two main areas which one is a cross correlation-based method and the other is a feature-based method. The former is based on motion estimation from video sequences. so there are a problem on rotating a camera about optical axis. In the latter method, it is difficult to match correspondence feature points correctly.'re find correct correspondences, we proposed the geometrical feature model and correspondence filters and the Gaussian distribution weight function to blend the images smoothly. The experiments show that our method is robust and effective.

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수정된 MAP 적응 기법을 이용한 음성 데이터 자동 군집화 (Automatic Clustering of Speech Data Using Modified MAP Adaptation Technique)

  • 반성민;강병옥;김형순
    • 말소리와 음성과학
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    • 제6권1호
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    • pp.77-83
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    • 2014
  • This paper proposes a speaker and environment clustering method in order to overcome the degradation of the speech recognition performance caused by various noise and speaker characteristics. In this paper, instead of using the distance between Gaussian mixture model (GMM) weight vectors as in the Google's approach, the distance between the adapted mean vectors based on the modified maximum a posteriori (MAP) adaptation is used as a distance measure for vector quantization (VQ) clustering. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method yields error rate reduction of 10.6% compared with baseline speaker-independent (SI) model, which is slightly better performance than the Google's approach.

A Greedy Merging Method for User-Steered Mesh Segmentation

  • Ha, Jong-Sung;Park, Young-Jin;Yoo, Kwan-Hee
    • International Journal of Contents
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    • 제3권2호
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    • pp.25-29
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    • 2007
  • In this paper, we discuss the mesh segmentation problem which divides a given 3D mesh into several disjoint sets. To solve the problem, we propose a greedy method based on the merging priority metric defined for representing the geometric properties of meaningful parts. The proposed priority metric is a weighted function using five geometric parameters, those are, a distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. In special, we can control by setting up the weight values of the proposed geometric parameters to obtain visually better mesh segmentation. Finally, we carry out an experiment on several 3D mesh models using the proposed methods and visualize the results.

동기 문제 해결을 위한 호핑 필터를 이용한 음성 보호 방식의 최적화에 관한 연구 (A Study on Optimization of Speech Encryption Scheme using Hopping Filter in order to Solve the Synchronization Problem)

  • 정지원;이경호;원동호
    • 한국통신학회논문지
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    • 제18권11호
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    • pp.1677-1688
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    • 1993
  • 호핑 필터를 이용한 이차원 진폭 스크램블링 알고리즘은 기존의 음성 보호 방식의 단점을 개선시킬 수 있는 아날로그 음성 신호에 있어서 강력한 보호 방식이다. 본 논문에서는 이차원 진폭 스크램블링 알고리즘의 최대 단점인 동기 문제를 해결하기 위하여 variable delay를 이용한 알고리즘을 제안하였다. 또한 PAM 신호를 가우시안 집음이 존재하는 채널로 전송하였을 때 수신단에서는 복원된 음성 신호의 왜곡을 분석함으로써 최적의 보호 알고리즘 및 최적의 SNR 값을 시뮬레이션을 이용하여 나타내었다.

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이미지 보간기법의 성능 개선을 위한 비국부평균 기반의 후처리 기법 (Non-Local Mean based Post Processing Scheme for Performance Enhancement of Image Interpolation Method)

  • 김동형
    • 디지털산업정보학회논문지
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    • 제16권3호
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    • pp.49-58
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    • 2020
  • Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

실시간 배경갱신 및 이를 이용한 객체추적 (Real time Background Estimation and Object Tracking)

  • 이완주
    • 정보학연구
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    • 제10권4호
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    • pp.27-39
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    • 2007
  • Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.

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독립성분 분석기법에 의한 심전도 신호의 왜곡 보정 (Suppressing Artefacts in the ECG by Independent Component Analysis)

  • 김정환;김경섭;김현태;이정환
    • 전기학회논문지
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    • 제62권6호
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    • pp.825-832
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    • 2013
  • In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.

가중특징 Mahalanobis거리를 이용한 마이크 어레이 음석인식의 성능향상 (Performance Improvement of Microphone Array Speech Recognition Using Features Weighted Mahalanobis Distance)

  • ;정현열
    • The Journal of the Acoustical Society of Korea
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    • 제29권1E호
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    • pp.45-53
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    • 2010
  • In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.

LiDAR 센서 신호 보정 및 노이즈 필터링 기술 개발 (Signal Compensation of LiDAR Sensors and Noise Filtering)

  • 박홍순;최준호
    • 센서학회지
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    • 제28권5호
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    • pp.334-339
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    • 2019
  • In this study, we propose a compensation method of raw LiDAR data with noise and noise filtering for signal processing of LiDAR sensors during the development phase. The raw LiDAR data include constant errors generated by delays in transmitting and receiving signals, which can be resolved by LiDAR signal compensation. The signal compensation consists of two stage. First one is LiDAR sensor calibration for a compensation of geometric distortion. Second is walk error compensation. LiDAR data also include fluctuation and outlier noise, the latter of which is removed by data filtering. In this study, we compensate for the fluctuation by using the Kalman filter method, and we remove the outlier noise by applying a Gaussian weight function.

단일 신호에 대한 창 함수의 잡음 제거 성능 평가 (Performance estimation of the noise reduction by window function on a single tone)

  • 백문열;김병삼
    • 한국정밀공학회지
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    • 제13권5호
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    • pp.38-43
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    • 1996
  • Windowing routines have as their purpose the reduction of the sidelobes of a spectral output of the FFT or DFT routines. Windowing routines accomplish this by forcing the beginning and end of any sequence to approach each other in value. Since they must work with any sequence they force the beginning and ending samples near zero. To make up for this reduction in power, windowing routines give extra weight to the values near the middle of the sequence. The difference between windows is the way in which they transition from the low weights near the edges to the higher weights neqr the middle of the sequence. Signal-to-noise ratio(SNR) can be determined by the ratio of the output noisy signal variance to the input noisy signal variance of a window. Standard deviation of noise is reduced by windowing. Thus, the windowing operation improved the SNR of the noisy signal. This paper shows a performance estimation of windowing on a single tone with added Gaussian noise and uniform noise.

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