Method to improve the accuracy of depth images based on differential entropy

YuJie Fang; Xia Wang; ZhiBin SunBingHua SuJunWen Xue

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Method to improve the accuracy of depth images based on differential entropy

机译：Method to improve the accuracy of depth images based on differential entropy

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摘要

The time-of-flight (TOF) camera has recently received significant attention due to its small size, low cost, and low-power consumption, which can be widely used in fields such as automatic navigation and machine vision. The TOF camera can calculate 3D information of targets with dozens of frames per second. However, poor accuracy still exists in the presence of various inevitable disturbances. In particular, the imaging distance and object reflectivity are remarkable factors. In this study, the depth imaging conditions, including ambient light, detection distance, and object reflectivity, are theoretically analyzed using differential entropy. Because many coupled factors disturb the imaging accuracy simultaneously, we propose a type of supervised learning machine, entropy-based k-nearest neighbor, based on differential entropy. Experiments show that this method can significantly improve the accuracy of depth data obtained by a TOF camera.

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来源
《Optical Engineering》 |2021年第3期|033105-1-033105-14|共14页
作者
YuJie Fang; Xia Wang; ZhiBin SunBingHua SuJunWen Xue;
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作者单位

Ministry of Education, Beijing Institute of Technology, School of Optics and Photonics, Key Laboratory of Optoelectronic Imaging Technology and Systems, Beijing, China;

Chinese Academy of Sciences, National Space Science Center, Beijing, China;

Beijing Institute of Technology, Zhuhai, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
关键词
image processing; three dimensions; machine learning; noise;

Method to improve the accuracy of depth images based on differential entropy

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