Multifeature fusion and mutual co-operative particle swarm optimization in three-dimensional reconstruction of belly

Genfu Xiao; Huan Liu; Chunjuan Ouyang; Yanling Jin

doi:10.1117/1.JEI.28.2.021003

31 December 2018 Multifeature fusion and mutual co-operative particle swarm optimization in three-dimensional reconstruction of belly

Genfu Xiao, Huan Liu, Chunjuan Ouyang, Yanling Jin

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 021003 (December 2018). https://doi.org/10.1117/1.JEI.28.2.021003

Abstract

A belly reconstruction and measurement scheme is conducted on a shape-flexible mannequin with digital image correlation technique. We adopted an integer subpixel image matching process. First, a compound feature including Gaussian combined moment and parameters extracted from gray-level co-occurrence matrix is proposed to track an integer pixel between reference and target deformed images. Second, a mutual learning co-operative particle swarm optimization algorithm is employed to locate the subpixel precisely. Each subpopulation does its own optimization independently. The subpopulations keep information communication and knowledge sharing for co-operative evolution to enhance global searching capability. In addition, the previous optimal information from the former interest point is adopted fully in initializing the particles’ positions of the next interest point, which effectively improves the speed of the convergence. Experimental results indicate that under the high measurement accuracy without any loss, the time-consumption of this scheme is significantly superior to that of the conventional method, particularly at a large number of interest points.

Citation Download Citation

Genfu Xiao, Huan Liu, Chunjuan Ouyang, and Yanling Jin "Multifeature fusion and mutual co-operative particle swarm optimization in three-dimensional reconstruction of belly," Journal of Electronic Imaging 28(2), 021003 (31 December 2018). https://doi.org/10.1117/1.JEI.28.2.021003

Received: 24 June 2018; Accepted: 9 October 2018; Published: 31 December 2018

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