Convolutional Deep Belief Networks for Single-CellObject Tracking in Computational Biology and Computer Vision
Joint Authors
Zhong, Bineng
Pan, Shengnan
Wang, Tian
Du, Jixiang
Chen, Duansheng
Cao, Liujuan
Zhang, Hongbo
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-26
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhong, Bineng& Pan, Shengnan& Zhang, Hongbo& Wang, Tian& Du, Jixiang& Chen, Duansheng…[et al.]. 2016. Convolutional Deep Belief Networks for Single-CellObject Tracking in Computational Biology and Computer Vision. BioMed Research International،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099297
Modern Language Association (MLA)
Zhong, Bineng…[et al.]. Convolutional Deep Belief Networks for Single-CellObject Tracking in Computational Biology and Computer Vision. BioMed Research International No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1099297
American Medical Association (AMA)
Zhong, Bineng& Pan, Shengnan& Zhang, Hongbo& Wang, Tian& Du, Jixiang& Chen, Duansheng…[et al.]. Convolutional Deep Belief Networks for Single-CellObject Tracking in Computational Biology and Computer Vision. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099297
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099297