Noise Reduction Approach of Range Image Using Nonlinear 2D Kalman Filter

Jun KATAYAMA; Yoshifumi SEKINE

Noise Reduction Approach of Range Image Using Nonlinear 2D Kalman Filter

Jun KATAYAMA, Yoshifumi SEKINE

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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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E85-A No.4 pp.770-775

Publication Date: 2002/04/01

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Type of Manuscript: Special Section PAPER (Special Section of Selected Papers from the 14th Workshop on Circuits and Systems in Karuizawa)

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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 -