主动轮廓模型的蜜蜂交配优化方案

M. Horng, Jin-Yi Chen, Ren-Jean Liou
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引用次数: 3

摘要

本文采用蜜蜂交配优化(HBMO)算法改进活动轮廓控制点连接凹区域的检测。在传统的主动轮廓模型(ACM)方法中,控制点的更新是基于其在小搜索窗口内的局部能量。因此,它总是导致无法精确搜索边界凹点。为了克服这些缺点,本文采用基于hbmo的蛇形方案,在每个控制点周围的更大搜索窗口中搜索最优位置。在该方案中,每条活动轮廓有一条包含多个基因的染色体以及活动轮廓的控制点。通过最小化活动轮廓的总能量来迭代地移动这些控制点。实验结果表明,基于hbmo的蛇形算法可以在不需要大量计算时间的情况下,更精确地定位出凹凸目标的边界。
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Honey Bee Mating Optimization Scheme for Active Contour Model
In this paper, the honey bee mating optimization (HBMO) algorithm is used to improve the detection of the concave region connected with the control points of active contour. In the traditional active contour model (ACM) method, the updating of control point is based on its local energy within a small searching window. As a result, it always results in the failure of precisely searching the boundary concavities. In order to vanquish these drawbacks, the HBMO-based snake scheme is adopted in this paper to search for the optimal position in a lager searching window around each control point. In this proposed scheme, to each active contour there is a chromosome that includes several genes as well as the control points of active contour. These control points are moved iteratively by minimizing the total energy of the active contour. Experimental results reveal that the proposed HBMO-based snake scheme can locate the object boundary of concavity more precisely without requiring large of computational time.
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