Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision.
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...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 16 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/25/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117669195&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117669195 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/25/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 117669195 117669195 117669195 10.1155/2016/8182416 117669195 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision. aug: au: Zhong, Bineng Pan, Shengnan Wang, Cheng Wang, Tian Du, Jixiang Chen, Duansheng Cao, Liujuan affil: Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China sug: subj: Image Processing, Computer Assisted Algorithms Factor Analysis Biomedical Engineering Neural Networks (Computer) ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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