Automatic initialization for naval application of graph segmentation techniques: A comparative study

Irene Camino, U. Zölzer
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Abstract

Nowadays, many different image processing applications are of high interest to maritime authorities because of security reasons. Depending on the application, different kinds of images are employed. The extraction of ship silhouettes requires high resolution images in order to obtain accurate results. However, when the characteristics of the naval environment are visible the background complexity increases greatly and automatic approaches fail. In order to overcome these difficulties we propose an automatic initialization for graph segmentation techniques. A comparative study of earlier suggested initializations for different graph segmentation techniques is also presented. It shows that, under such unfavorable image conditions, finding the proper initialization in an automatic way is not trivial. Yet, the precision and recall achieved by our initialization are considerable higher regardless the graph segmentation. Furthermore, the performance is highly increased since the best results are obtained after only the first iteration.
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图形分割技术在舰船中的自动初始化应用:比较研究
目前,由于安全原因,许多不同的图像处理应用引起了海事当局的高度兴趣。根据应用程序的不同,使用不同类型的图像。船舶轮廓的提取需要高分辨率的图像才能获得准确的结果。然而,当海军环境的特征可见时,背景复杂性大大增加,自动方法失败。为了克服这些困难,我们提出了一种自动初始化的图形分割技术。对早期提出的不同图分割技术的初始化进行了比较研究。这表明,在如此不利的图像条件下,以自动方式找到合适的初始化并不容易。然而,无论图形分割如何,我们的初始化所获得的精度和召回率都相当高。此外,由于仅在第一次迭代之后就获得了最佳结果,因此性能得到了极大的提高。
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