Image classification and recognition method to mechanical parts based on fractal dimension

2017 International Conference on Mechanical, System and Control Engineering (ICMSC) Pub Date : 2017-05-01 DOI:10.1109/ICMSC.2017.7959444

Tao He, Kun Yu, Lang Chen, Kexue Lai, Liangen Yang, Xuanze Wang, Zhongsheng Zhai

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引用次数: 1

Abstract

Complex mechanical parts have characteristics of irregularity and certain statistical self-similarity, which can be described by fractal dimension. And the values of their fractal dimension can be used as an measurement to classify and recognize the mechanical parts. In addition, the values can guide robots to grab parts. However, the image obtained by a vision system, which contains part images main image and image background will affect the calculation of fractal dimension of main images. In order to solve the problem, an improved differential box-counting method is designed in this paper. The fractal dimension of part images which has been cut and rotated can be calculated using this differential box- counting method. The experimental result shows that the improved differential box-counting method can calculate the fractal dimension of different size-length images, and the values are more stable. The improved method solves the problem that traditional algorithm can only calculate the fractal dimension of image which side length is integer power of 2.

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基于分形维数的机械零件图像分类识别方法

复杂机械零件具有不规则性和一定的统计自相似性，可以用分形维数来描述。它们的分形维数值可以作为机械零件分类和识别的度量。此外，这些数值还可以指导机器人抓取零件。然而，视觉系统获得的图像包含部分图像、主图像和图像背景，会影响主图像分形维数的计算。为了解决这一问题，本文设计了一种改进的微分计数法。利用这种微分计数法，可以计算出经过切割和旋转的零件图像的分形维数。实验结果表明，改进的微分盒计数方法可以计算不同尺寸长度图像的分形维数，且数值更加稳定。改进的方法解决了传统算法只能计算边长为2的整数次幂的图像分形维数的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 International Conference on Mechanical, System and Control Engineering (ICMSC)

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