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A novel 3D shape reconstruction method based on maximum correntropy Kalman filtering

Man Chen (Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China and University of Chinese Academy of Sciences, Beijing, China)
Yong Zhong (Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China and University of Chinese Academy of Sciences, Beijing, China)
Zhendong Li (Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China and University of Chinese Academy of Sciences, Beijing, China)
Jin Wu (School of Automation, University of Electronic Science and Technology of China, Chengdu, China)

Sensor Review

ISSN: 0260-2288

Article publication date: 28 November 2018

Issue publication date: 17 May 2019

145

Abstract

Purpose

This paper aims to investigate a novel shape from focus (SFF) algorithm based on maximum correntropy Kalman filtering (SFF-MCKF) for solving the problem that traditional SFF methods are weak in de-noising and spatial continuity.

Design/methodology/approach

To be specific, it was first assumed that the predicted depth of next pixel is equal to the depth of the current pixel according to spatial continuity. Besides, the observing data are derived from the estimation of traditional SFF and the corresponding covariance of noise is adaptively calculated by the entropy along the optical axis. Finally, to enhance robustness, we systematically conduct MCKF iteratively in four transfer directions that are 0°, 90°, 45° and −45°, respectively.

Findings

The experimental results indicate that the robustness of SFF-MCKF facing noises as well as the spatial continuity is better than that of the existing representative ones.

Research limitations/implications

As the proposed method is aimed at precision objects, high demand is for experimental device. Unstable device unregister the source images, which is unfavorable for observing data.

Originality/value

SFF-MCKF can be applied to many precision object measurements without significant drifts, such as the surface reconstruction of metal objects and electronic components. Besides, the computation cost is low, and SFF-MCKF has a wide range of real-time industrial applications.

Keywords

Acknowledgements

This research was supported by Sichuan Administration of Science and Technology under Grant No. 2014CC0043 and The Science & Technology Project of Sichuan under Grant No. SCMZ2006012. The author(s) of this article have not made their research data set openly available. Any enquiries regarding the data set can be directed to the corresponding author.

Citation

Chen, M., Zhong, Y., Li, Z. and Wu, J. (2019), "A novel 3D shape reconstruction method based on maximum correntropy Kalman filtering", Sensor Review, Vol. 39 No. 3, pp. 332-340. https://doi.org/10.1108/SR-07-2018-0168

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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