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所属单位：理学院

发表刊物：MEASUREMENT

关键字：Motion blur Vision based measurement Inner-frame path Trans-frame path Blurred targets recognition 3D reconstruction

摘要：Vision-based geometry measurement plays a crucial role in many science and industrial areas. Plenty of researches devoted to measuring static objects, while few focused on motion blurred situations, which inevitably arise when the object being measured moves fast relative to the camera(s). Motion blur usually invalids the vision-based measurement algorithms designated for static objects. In this paper, we devote to accurate three dimensional (3D) reconstruction of moving objects from motion blurred stereo image pairs. A convolutional neural network (CNN) based method is first proposed to recognize the motion blurred visual targets. A motion blur model based on inner-frame path superposition imaging is then established. Finally, an optimization framework is set up to reconstruct the 3D target motion path during the camera exposure. Experiments are involved to demonstrate the validity and accuracy of the method. (C) 2019 Elsevier Ltd. All rights reserved.

ISSN号：0263-2241

是否译文：否

发表时间：2019-10-01

合写作者：Chen, Mingjun,张丽艳,叶南,Tao, Cong

通讯作者：张丽艳,周含策