基于分数布朗运动的图象目标检测

本文分析了处处连续而不可导的非平稳不规则信号模型—分数布朗运动(fBm)。fBm具有时间(或空间)长程相关性和指数功率谱特性,在大尺度轮廓上,有较大的相关性;而观察尺度越小,变化幅度越小,越表现丰富的细节。这在自然景物、图形建模方面具有很好的应用前景。同时自然景物的较好符合fBm的分形模型与人造物体不符合分形模型之间的差异可以用来进行图象分割。文中给出了一个基于fBm分形参数的图象目标检测方案,并给出了实验结果。

IMAGE OBJECT DETECTION BASED ON FRACTIONAL BROWNIAN MOTION

Fractional Brownian motion, continuous everywhere and differentiable nowhere, offers a convenient modeling for irregular nonstationary stochastic processes with long-term dependencies and power law behavior of spectrum over wide ranges of frequencies. It shows high correlation at coarse scale and varies slightly at fine scale, which is suitable for and successful in describing and modeling natural scenes. On the other hand, man-made objects can be constructively well described by using a set of regular simple shape primitives such as line, cylinder, etc. and are free of fractal. Based on the difference, a method to discriminate man-made objects from natural scenes is provided. Experimental results verify its efficiency.