Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26256
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Gao, Fei
Xue, Xiangshang
Wang, Jun
Sun, Jinping
Hussain, Amir
Yang, Erfu
Contact Email: ahu@cs.stir.ac.uk
Title: Visual attention model with a novel learning strategy and its application to target detection from SAR images
Editors: Liu, CL
Hussain, A
Luo, B
Tan, KC
Zeng, Y
Zhang, Z
Citation: Gao F, Xue X, Wang J, Sun J, Hussain A & Yang E (2016) Visual attention model with a novel learning strategy and its application to target detection from SAR images In: Liu CL, Hussain A, Luo B, Tan KC, Zeng Y, Zhang Z (ed.) Advances in Brain Inspired Cognitive Systems. BICS 2016, Cham, Switzerland: Springer. BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems, 28.11.2016 - 30.11.2016, Beijing, China, pp. 149-160.
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science, 10023
Conference Name: BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems
Conference Dates: 2016-11-28T00:00:00Z
Conference Location: Beijing, China
Abstract: The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm.
Status: Book Chapter: publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: https://link.springer.com/chapter/10.1007/978-3-319-49685-6_14

Files in This Item:
File Description SizeFormat 
Gao_etal_LNCS_2016.pdf2.42 MBAdobe PDFUnder Embargo until 31/12/2999     Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.