Visual tracking using convolutional features with sparse coding-Reference-Cited by-同舟云学术

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Visual tracking using convolutional features with sparse coding

Published:2020-11-27 Issue:5 Volume:54 Page:3349-3360
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Abbass Mohammed Y.,Kwon Ki-Chul,Kim Nam,Abdelwahab Safey A.,El-Samie Fathi E. Abd,Khalaf Ashraf A. M.

Funder

Cross-Ministry Giga KOREA Project' grant funded by the Korea government

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-020-09905-7.pdf

Reference32 articles.

1. Abbass MY, Kim H (2018) Blind image separation using pyramid technique. EURASIP J Image Video Proc 2018(1)

2. Abbass MY, Kim H, Abdelwahab SA, Haggag SS, El-Rabaie EM, Dessouky MI, Abd Fathi E, El-Samie (2019) Image deconvolution using homomorphic technique. Signal, Image and Video Processing 13(4):703–709

3. Abbass MY, Kwon K, Kim N et al (2020a) A survey on online learning for visual tracking. Vis Comput. https://doi.org/10.1007/s00371-020-01848-y

4. Abbass MY, Kwon K, Kim N et al (2020b) Efficient object tracking using hierarchical convolutional features model and correlation filters. Vis Comput. https://doi.org/10.1007/s00371-020-01833-5

5. Abbass MY, Kwon K, Alam MS et al (2020c) Image super resolution based on residual dense CNN and guided filters. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09824-3

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