The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, discutimos abordagens de redução de ruído para melhorar imagens de alcance usando um filtro de Kalman 2D não linear. Primeiro, propomos o filtro Kalman 2D não linear, que pode reduzir o ruído na imagem de alcance usando um vetor de aresta estimado e uma função não linear que não distorce arestas vivas. Em segundo lugar, avaliamos a redução do ruído aditivo em uma imagem de faixa de teste usando o erro quadrático médio (MSE). Terceiro, discutimos a taxa de detecção e o número de falsas detecções na imagem de alcance estimado. Quarto, é apresentado um exemplo de simulação que demonstra o desempenho do filtro de Kalman 2D proposto para uma imagem de alcance real com mudanças abruptas. Por fim, são apresentados resultados de simulação que mostram que a imagem estimada do filtro de Kalman 2D não linear é eficaz na redução da quantidade de ruído, ao mesmo tempo que causa suavização mínima das mudanças abruptas.
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Jun KATAYAMA, Yoshifumi SEKINE, "Noise Reduction Approach of Range Image Using Nonlinear 2D Kalman Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 4, pp. 770-775, April 2002, doi: .
Abstract: In this paper, we discuss noise reduction approaches to improving range images using a nonlinear 2D Kalman filter. First, we propose the nonlinear 2D Kalman filter, which can reduce noise in the range image using an estimated edge vector and a nonlinear function that does not distort sharp edges. Second, we evaluate reduction of the additive noise in a test range image using the mean square error (MSE). Third, we discuss the detection rate and the number of false detections in the estimated range image. Fourth, a simulation example demonstrating the performance of the proposed 2D Kalman filter for a real range image having abrupt changes is presented. Finally, simulation results are presented which show that the estimated image of the nonlinear 2D Kalman filter is effective in reducing the amount of noise, while causing minimal smoothing of the abrupt changes.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_4_770/_p
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@ARTICLE{e85-a_4_770,
author={Jun KATAYAMA, Yoshifumi SEKINE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Noise Reduction Approach of Range Image Using Nonlinear 2D Kalman Filter},
year={2002},
volume={E85-A},
number={4},
pages={770-775},
abstract={In this paper, we discuss noise reduction approaches to improving range images using a nonlinear 2D Kalman filter. First, we propose the nonlinear 2D Kalman filter, which can reduce noise in the range image using an estimated edge vector and a nonlinear function that does not distort sharp edges. Second, we evaluate reduction of the additive noise in a test range image using the mean square error (MSE). Third, we discuss the detection rate and the number of false detections in the estimated range image. Fourth, a simulation example demonstrating the performance of the proposed 2D Kalman filter for a real range image having abrupt changes is presented. Finally, simulation results are presented which show that the estimated image of the nonlinear 2D Kalman filter is effective in reducing the amount of noise, while causing minimal smoothing of the abrupt changes.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Noise Reduction Approach of Range Image Using Nonlinear 2D Kalman Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 770
EP - 775
AU - Jun KATAYAMA
AU - Yoshifumi SEKINE
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2002
AB - In this paper, we discuss noise reduction approaches to improving range images using a nonlinear 2D Kalman filter. First, we propose the nonlinear 2D Kalman filter, which can reduce noise in the range image using an estimated edge vector and a nonlinear function that does not distort sharp edges. Second, we evaluate reduction of the additive noise in a test range image using the mean square error (MSE). Third, we discuss the detection rate and the number of false detections in the estimated range image. Fourth, a simulation example demonstrating the performance of the proposed 2D Kalman filter for a real range image having abrupt changes is presented. Finally, simulation results are presented which show that the estimated image of the nonlinear 2D Kalman filter is effective in reducing the amount of noise, while causing minimal smoothing of the abrupt changes.
ER -