对象的边界是图象识别的主要依据,但若对象处于一定的自然背景中,则自然背景的边界会给识别过程增加计算的负担,因此在识别前有必要将对象与背景的边界区别开.该问题通常是通过基于“试探性阈值的方法”来解决,由于该方法未考虑边界点的局部特性,故该方法不能保留对象的细节.为了解决这一问题,提出了一种将人造物边界从自然背景中分离出来的基于分形几何的新算法.该算法基于对边界点的梯度强度阈值与曲线分形维数的考虑,用聚类分析的方法对边界点进行筛选,并利用以云,树丛为背景的飞机图象为实验对象,来验证该算法的有效性与优越性.在这些例子中,可以看到大量自然背景的边界被滤掉了,而飞机的局部细节得到了保留.最后又进一步探讨了该算法的适用范围.

The most important information for image identification is edges of objects. But when objects are seated among nature background, those edges of nature background will be a great burden to the identification process. So unnecessary edges must be removed before identification. In tradition, a method called“huerestic threshold”is used to reach that target. But it performs bad in preserving details of objects, because local information is not taken into account. According to fractal geometry, it is found that the fractal density of edges of men-made objects is close to 1, while that of nature background is close to 0 or 2. Thus a novel method is proposed to fulfill the demand. This method integrates the consideration of both gradient strength of edge points and fractal density of curves, and uses cluster analysis for filter. In this paper, images of planes with cloud and forest as background are used to testify the effectiveness of the new method. From the experiment, it can be seen that most background edges are filtered, yet details of planes are preserved. so the method is beyond the function of the traditional method. At the end of this paper, the limit of this method is discussed.