发表状态 | 已发表Published |
题名 | Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision |
作者 | |
发表日期 | 2016 |
发表期刊 | BioMed Research International
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ISSN/eISSN | 2314-6133 |
卷号 | 2016 |
摘要 | Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of a target by exploiting both classic principal component analysis (PCA) algorithms with recent deep learning representation architectures. We use learned PCA eigenvectors as filters and develop a novel algorithm to represent a target by composing of a PCA-based filter bank layer, a nonlinear layer, and a patch-based pooling layer, respectively. Then, based on the feature representation, a neural network with one hidden layer is trained in a supervised mode to construct a discriminative appearance model. Finally, to alleviate the tracker drifting problem, a sample update scheme is carefully designed to keep track of the most representative and diverse samples during tracking. We test the proposed tracking method on two standard individual cell/object tracking benchmarks to show our tracker's state-of-the-art performance. |
DOI | 10.1155/2016/8182416 |
URL | 查看来源 |
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:000382628500001 |
Scopus入藏号 | 2-s2.0-84985963054 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/7272 |
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, Fujian Province, 361021, China 2.School of Information Science and Technology, Xiamen University, China |
推荐引用方式 GB/T 7714 | Zhong, Bineng,Pan, Shengnan,Wang, Chenget al. Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision[J]. BioMed Research International, 2016, 2016. |
APA | Zhong, Bineng., Pan, Shengnan., Wang, Cheng., Wang, Tian., Du, Jixiang., .. & Cao, Liujuan. (2016). Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision. BioMed Research International, 2016. |
MLA | Zhong, Bineng,et al."Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision". BioMed Research International 2016(2016). |
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