Robust Individual-CellObject Tracking via PCANet Deep Network in Biomedicine and Computer Vision
المؤلفون المشاركون
Zhong, Bineng
Pan, Shengnan
Wang, Cheng
Wang, Tian
Du, Jixiang
Chen, Duansheng
Cao, Liujuan
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-08-25
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhong, Bineng& Pan, Shengnan& Wang, Cheng& Wang, Tian& Du, Jixiang& Chen, Duansheng…[et al.]. 2016. Robust Individual-CellObject Tracking via PCANet Deep Network in Biomedicine and Computer Vision. BioMed Research International،Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1098995
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhong, Bineng…[et al.]. Robust Individual-CellObject Tracking via PCANet Deep Network in Biomedicine and Computer Vision. BioMed Research International No. 2016 (2016), pp.1-15.
https://search.emarefa.net/detail/BIM-1098995
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhong, Bineng& Pan, Shengnan& Wang, Cheng& Wang, Tian& Du, Jixiang& Chen, Duansheng…[et al.]. Robust Individual-CellObject Tracking via PCANet Deep Network in Biomedicine and Computer Vision. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1098995
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1098995
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر