|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Visual attention model with a novel learning strategy and its application to target detection from SAR images|
|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|
|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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|Gao_etal_LNCS_2016.pdf||Fulltext - Published Version||2.42 MB||Adobe PDF||Under Embargo until 3000-10-14 Request a copy|
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