改进的分形算法在弱小目标检测中的应用  

Application of improved fractal algorithm in small target detection

作　　者：王鑫[1] 唐振民[1] 

机构地区：[1]南京理工大学计算机科学与技术学院,江苏南京210094

出　　处：《系统工程与电子技术》2009年第9期2071-2075,共5页Systems Engineering and Electronics

摘　　要：针对单帧图像中的弱小目标检测问题,提出了一种改进的基于分形的快速检测方法。该方法首先利用图像的局部熵信息对目标进行粗定位,以得到一个包含目标的感兴趣区域,然后利用分形理论构造该区域的分维像,最后对分维像采用自适应阈值分割即可将弱小目标精确检测出来。与传统分形算法相比,提出的改进算法包含粗定位和细定位两部分,它将分形算法要处理的区域缩减到局部熵所估计的小范围内,从而克服了传统分形方法计算量大、抗噪性差的缺点。仿真实验结果表明,该方法能够稳健、快速、有效地检测弱小目标。An improved fast method based on fractal theory is presented for small and weak target detection in a single-frame image. The algorithm firstly uses the local entropy information to locate the target coarsely, and then a region of interest （ROD containing the small target is obtained. Secondly, a fractal dimension image of this region is constructed based on the fractal theory. Finally, self-adaptive threshold segmentation is used for the fractal dimension image to get the exact detection result. Compared with the traditional fractal algorithm, the presented method is divided into two parts： coarse location and accurate location. The region to be processed by the fractal algorithm is reduced to a small range that is estimated by local entropy, thus overcoming the defect of huge computational cost and poor anti-noise capability of traditional fractal methods. The experimental results prove that the proposed method is robust, fast and effective for small and weak target detection.

关 键 词：弱小目标检测 分形 局部熵 感兴趣区域 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]