科研成果详情

发表状态已发表Published
题名Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision
作者
发表日期2016
发表期刊BioMed Research International
ISSN/eISSN2314-6133
卷号2016
摘要

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

DOI10.1155/2016/9406259
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收录类别SCIE
语种英语English
WOS研究方向Biotechnology & Applied Microbiology ; Research & Experimental Medicine
WOS类目Biotechnology & Applied Microbiology ; Medicine, Research & Experimental
WOS记录号WOS:000387397600001
Scopus入藏号2-s2.0-84994499990
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/7271
专题个人在本单位外知识产出
通讯作者Zhong, Bineng
作者单位
1.Department of Computer Science and Engineering, Huaqiao University, Xiamen, China
2.School of Information Science and Technology, Xiamen University, Xiamen, China
推荐引用方式
GB/T 7714
Zhong, Bineng,Pan, Shengnan,Zhang, Hongboet al. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision[J]. BioMed Research International, 2016, 2016.
APA Zhong, Bineng., Pan, Shengnan., Zhang, Hongbo., Wang, Tian., Du, Jixiang., .. & Cao, Liujuan. (2016). Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision. BioMed Research International, 2016.
MLA Zhong, Bineng,et al."Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision". BioMed Research International 2016(2016).
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