摘要
在传统的基于表面积的图像分形维数计算中,不同尺度下的表面积计算均在原图像中进行。这与图像细节随空间尺度的变化而变化的事实不符,据此计算的同类地物的分数维变化范围较大,对基于分形的图像分割、分类产生不利影响。针对这一问题,提出一种基于面积加权的快速插值算法来模拟不同尺度下的遥感图像,进而计算图像的分数维。实验结果表明,对于大小为512pixel×512pixel的标准Lena图像来说,新算法的插值速度提高10倍以上,且得到的分数维具有更小的类内方差以及更好的抗噪性,因而更适用于基于分形的遥感图像分割、分类。
All computation of surface area of images in various scales is performed in the original image by conventional computing method of fractal dimension. The algorithms are inconsistent with the fact that the details of image could change when the metric scale varies, as a result, the span of the fractal dimension in the same kind of ground object is too big and goes against the image segmentation and image classification. To solve this problem, a fast interpolation algorithm based on area weight is put forward to simulate the images with various scales, which is used to calculate the fractal dimension of image. Experimental results show that the proposed method speeds up to 10 times faster and has less variance within clusters than the conventional method according to the standard Lena image. In addition, it can resist image noise well. Therefore, the proposed algorithm is applicable to image segmentation or image classfication.
出处
《激光与光电子学进展》
CSCD
北大核心
2013年第9期56-62,共7页
Laser & Optoelectronics Progress
基金
国家973计划(2013CB733405)
国家自然科学基金(41175015)
关键词
图像处理
分形
加权插值
纹理图像
图像分割
image processing
fractal
weighted interpolation
texture image
image segmentation
