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浏览/检索结果: 共3条，第1-3条 帮助 限定条件 内容类型：会议论文     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 3D Object Classification Based on Multi Convolutional Neural Networks 会议论文 INTERNATIONAL CONFERENCE ON APPLIED MECHANICS AND MECHANICAL AUTOMATION (AMMA 2017), 2017-01-01 作者:  Lu, Mei-qi;  Li, Wei;  Ning, Ya-guang 收藏  |  浏览/下载：4/0  |  提交时间：2019/12/30 3D object classification  Convolutional neural network  Image classification   3D Attention-Driven Depth Acquisition for Object Identification 会议论文 ACM SIGGRAPH Asia Conference, 2016 作者:  Xu, Kai;  Shi, Yifei;  Zheng, Lintao;  Zhang, Junyu;  Liu, Min 收藏  |  浏览/下载：4/0  |  提交时间：2019/12/31 3D acquisition  depth camera  next-best-view  object identification  attention-based model  shape classification   Research on station mode for attitude determination of multiple targets (EI CONFERENCE) 会议论文 2010 International Conference on Computational and Information Sciences, ICCIS2010, December 17, 2010 - December 19, 2010, Chengdu, Sichuan, China Li M.; Wang J.; Zhao L. 收藏  |  浏览/下载：27/0  |  提交时间：2013/03/25 A key problem of attitude determination system is how to locate CMOS cameras and deal with relations among them. In this paper  several kinds of multi-CMOS station mode are introduced. Fundamental principles and classification of station mode directly affect on measurement parameters of system. No matter what mode is designed  the ultimate goal is to extend Effective Viewing Field (EVF)  increase capture ratio of objects and increase measurement precision. Station mode introduced in this paper is made of CMOS cameras  because of low cost  high speed frame frequency and low power consumption  CMOS cameras were putted near from object tested. So sequences of image are legible  which are convenient for analysis of three dimensional(3D) attitude. 2010 IEEE.