基于可变形模板的水下声图像分割
Underwater acoustic image segmentation based on deformable template
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摘要: 针对水下声成像中的形变和边缘模糊等问题,在分析现有模板匹配算法存在问题的基础上,提出了一种基于可变形模板的水下声图像分割算法。该算法以Snake模型的最小能量思想为基础,对原有的能量函数重新进行了定义,引入了形状约束,从而使该算法对噪声的敏感程度相应下降,提高了算法的鲁棒性和分割效率。利用Dijkstra算法求取了能量函数的最优解,并对采用填充算法预处理后的实测声图像进行了图像分割实验。实验结果表明,相对于Snake模型和传统的寻优方法,该方法具有速度快、精度高和对形变、噪声不敏感的优点。Abstract: In order to solve the problem of deformation and blurry edge in underwater acoustic image segmentation, an approach based on the deformable template is presented. Compared with the energy minimization of the Snake model, the energy function is redefined by adding a shape restriction. This improves the noise-resistance ability so that robustness and high segmentation rate are acquired. The energy minimization problem is tackled using the Dijkstra Algorithm. This method has been successfully tested on the filled-in acoustic images. The results show that this algorithm is efficient, precise and very immune to image deformation and noise when compared to results obtained from Snake model and several traditional optimization methods.