科研成果详情

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
ISSN/eISSN2314-6133
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.

DOI10.1155/2016/8182416
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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:000382628500001
Scopus入藏号2-s2.0-84985963054
引用统计
被引频次:5[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/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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