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
Author(s): 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
Editor(s): 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 C, Hussain A, Luo B, Tan K, Zeng Y & Zhang Z (eds.) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science, 10023. BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems, Beijing, China, 28.11.2016-30.11.2016. Cham, Switzerland: Springer, pp. 149-160. https://doi.org/10.1007/978-3-319-49685-6_14
Issue Date: 2016
Date Deposited: 30-Nov-2017
Series/Report no.: Lecture Notes in Computer Science, 10023
Conference Name: BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems
Conference Dates: 2016-11-28 - 2016-11-30
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: VoR - Version of Record
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