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20 February 2012 Biological visual attention guided automatic image segmentation with application in satellite imaging
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Proceedings Volume 8291, Human Vision and Electronic Imaging XVII; 82911N (2012) https://doi.org/10.1117/12.911996
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States
Abstract
Taking inspiration from the significantly superior performance of humans to extract and interpret visual information, the exploitation of biological visual mechanisms can contribute to the improvement of the performance of computational image processing systems. Computational models of visual attention have already been shown to significantly improve the speed of scene understanding by attending only the regions of interest, while distributing the resources where they are required. However, there are only few attention-based computational systems that have been used in practical applications dealing with real data and up to now, none of the computational attention models was demonstrated to work under a wide range of image content, characteristics and scales such as those encountered in satellite imaging. This paper outlines some of the difficulties that the current generation of visual attention-inspired models encounter when dealing with satellite images. It then proposes a novel algorithm for automatic image segmentation and regions of interest search that combines elements of human visual attention with Legendre moments applied on the probability density function of color histograms. The experimental results demonstrate that the proposed approach obtains better results than one of the most evolved current computational attention model proposed in the literature.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. I. Sina, A.-M. Cretu, and P. Payeur "Biological visual attention guided automatic image segmentation with application in satellite imaging", Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 82911N (20 February 2012); https://doi.org/10.1117/12.911996
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