Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.
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 (C...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 15 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/26/2016
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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=119085689&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119085689 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/26/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 119085689 119085689 119085689 10.1155/2016/9406259 119085689 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision. aug: au: Zhong, Bineng Pan, Shengnan Zhang, Hongbo Wang, Tian Du, Jixiang Chen, Duansheng Cao, Liujuan affil: Department of Computer Science and Engineering, Huaqiao University, Xiamen, China sug: subj: Bioinformatics Methods Cells Analysis Computers and Computerization Probability Algorithms Methods Algorithms Evaluation Precision Descriptive Statistics Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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