彩色图像分形维数估计的新方法

Abadhan Ranganath, J. Mishra
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引用次数: 4

摘要

分形维数最早由Mandelbort[1]提出。分形维数描述了物体的形状和外观,具有自相似的性质。利用自相似的概念计算了多个对象的分形维数。由于分形物体与原始物体具有自相似性,且尺寸随尺度长度的变化不大。我们的主要目的是寻找图像的平滑度和粗糙度,并对图像进行分析。提出了多种灰度图像分形维数估计方法。介绍了利用分形维数方法求图像粗糙度和平滑度的几种现有方法。利用现有的分形维数方法进行了大量的实验,得到了各种各样的结果。在这篇报告中,我们描述了一些被提出的寻找彩色图像分形维数的方法。利用差分盒计数(DBC)法、细胞计数法和本文提出的方法分别对灰度图像和彩色图像进行分形维数计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Approach for Estimating Fractal Dimension of Both Gary and Color Images
Fractal Dimension was firstly introduced by Mandelbort [1]. Fractal Dimension describe about the shape and appearance of object, which have the property of self similarity. Fractal Dimension of several objects are calculated by using the concept of self similarity. Because Fractal objects are self similar to the original object and dimensions are little varies as per scale length. Our main purpose is to find smoothness and roughness of images and image analysis. Various methods were proposed to estimate the fractal dimension of Grey scale images. Some existing methods were described using Fractal Dimension methodology for finding the roughness and smoothness of images. So many experiments has been done by using existing methods of fractal dimension and found various results. In this report we have described some proposed approach for finding the fractal dimension of color images. We found out fractal dimension of both gray scale and color image using Differential Box Count (DBC) method, cell counting method and our proposed approach.
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