基于分形特征参数的目标边缘检测算法

Edge Detection Algorithm Based on Fractal Features

图像的边缘提取在计算机视觉系统的初级处理中具有关键作用,但目前仍是"瓶颈"问题。本文主要针对复杂背景下的目标边缘检测问题,提出了一种新的基于分形特征参数的目标边缘检测方法。算法基于分数布朗运动的方差性质提出一种改进的分形维数及其截距特征参数的计算方法,进而将该分形维数与截距特征参数相结合,提出了一种基于分形特征参数的边缘检测算法。实验表明,算法可以有效地实现目标边缘检测,同时算法的运算效率得到提高。

The edge extraction from images plays a key role in the elementary processing of computer vision system, but it＇s still a bottleneck problem. This thesis mainly paid attention to the issues related to the detection of the edges from the complicated nature background, and an improved method for edge detecting was presented based on the fractal features Furthermore, a new method about how to calculate the fractal dimension and the intercept feature was proposed by applying the property of fractional Brownian motion to the algorithm proposed. Then, based on the fractal dimension and intercept features, a measure method for discriminating edges of man-made object from natural scenes was provided. Experiments show that the proposed algorithm can detect the edges effectively, at the same time the runtime is reduced greatly