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Reducing Computational Complexity for Local Maxima Detection Using Facet Model  

Lee, Gyoon-Jung (부산대학교)
Park, Ji-Hwan (부산대학교)
Joo, Jae-Heum (부산가톨릭대학교)
Nam, Ki-Gon (부산대학교)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.13, no.3, 2012 , pp. 130-135 More about this Journal
Abstract
In this paper, we propose a technique to detect the size and location of the small target in images by using Gaussian kernel repeatedly. In order to detect the size and location of the small target, we find the local maximum value by applying the facet model and then use the $3{\times}3$ Gaussian kernel repeatedly. we determine the size of small target by comparing the local maximum value $D_2$ according to the number of iteration. To reduce the computational complexity, we use the Gaussian pyramid when using the kernel repeatedly. Through the experiment, we verified that the size and location of the small target is detected by the number of iterations and results show improvements from conventional methods.
Keywords
Small target detection; Facet Model; Gaussian kernel; Gaussian pyramid;
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