数字图像处理外文翻译参考文献

数字图像处理外文翻译参考文献

(文档含中英文对照即英文原文和中文翻译)

原文：

Application Of Digital Image Processing In The Measurement Of Casting Surface

Roughness

Ahstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parameters

and the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.

Keywords-casting surface; roughness measurement; image processing; feature parameters

Ⅰ.INTRODUCTION

Nowadays the demand for the quality and surface roughness of machining is highly increased, and the machine vision inspection based on image processing has become one of the hotspot of measuring technology in mechanical industry due to their advantages such as non-contact, fast speed, suitable precision, strong ability of anti-interference, etc [1,2]. As there is no laws about the casting surface and the range of roughness is wide, detection parameters just related to highly direction can not meet the current requirements of the development of the photoelectric technology, horizontal spacing or roughness also requires a quantitative representation. Therefore, the three-dimensional evaluation system of the casting surface roughness is established as the goal [3,4], surface roughness measurement based on image processing technology is presented. Image preprocessing is deduced through the image enhancement processing, the image binary conversation. The three-dimensional roughness evaluation based on the feature parameters is performed . An automatic detection interface of casting surface roughness based on MA TLAB is compiled which provides a solid foundation for the online and fast detection of casting surface roughness.

II. CASTING SURFACE IMAGE ACQUISITION SYSTEM

The acquisition system is composed of the sample carrier, microscope, CCD camera, image acquisition card and the computer. Sample carrier is used to place tested castings. According to the experimental requirements, we can select a fixed carrier and the sample location can be manually transformed, or select curing specimens and the position of the sampling stage can be changed. Figure 1 shows the whole processing procedure.,Firstly,the detected castings should be placed in the illuminated backgrounds as far as possible, and then through regulating optical lens,

setting the CCD camera resolution and exposure time, the pictures collected by CCD are saved to computer memory through the acquisition card. The image preprocessing and feature value extraction on casting surface based on corresponding software are followed. Finally the detecting result is output.

III. CASTING SURFACE IMAGE PROCESSING

Casting surface image processing includes image editing, equalization processing, image enhancement and the image binary conversation,etc. The original and clipped images of the measured casting is given in Figure 2. In which a) presents the original image and b) shows the clipped image.

A. Image Enhancement

Image enhancement is a kind of processing method which can highlight certain image information according to some specific needs and weaken or remove some unwanted informations at the same time[5].In order to obtain more clearly contour of the casting surface equalization processing of the image namely the correction of the image histogram should be pre-processed before image segmentation processing. Figure 3 shows the original grayscale image and equalization processing image and

their histograms. As shown in the figure, each gray level of the histogram has substantially the same pixel point and becomes more flat after gray equalization processing. The image appears more clearly after the correction and the contrast of the image is enhanced.

Fig.2 Casting surface image

Fig.3 Equalization processing image

B. Image Segmentation

Image segmentation is the process of pixel classification in essence. It is a very important technology by threshold classification. The optimal threshold is attained through the instmction thresh = graythresh (II). Figure 4 shows the image of the binary conversation. The gray value of the black areas of the Image displays the portion of the contour less than the threshold (0.43137), while the white area shows the gray value greater than the threshold. The shadows and shading emerge in the bright region may be caused by noise or surface depression.

Fig4 Binary conversation

IV. ROUGHNESS PARAMETER EXTRACTION

In order to detect the surface roughness, it is necessary to extract feature parameters of roughness. The average histogram and variance are parameters used to characterize the texture size of surface contour. While unit surface's peak area is parameter that can reflect the roughness of horizontal workpiece.And kurtosis parameter can both characterize the roughness of vertical direction and horizontal

direction. Therefore, this paper establishes histogram of the mean and variance, the unit surface's peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface is compiled based on MATLAB. Figure 5 shows the detection interface of surface roughness. Image preprocessing of the clipped casting can be successfully achieved by this software, which includes image filtering, image enhancement, image segmentation and histogram equalization, and it can also display the extracted evaluation parameters of surface roughness.

Fig.5 Automatic roughness measurement interface

V. CONCLUSIONS

This paper investigates the casting surface roughness measuring method based on digital Image processing technology. The method is composed of image acquisition, image enhancement, the image binary conversation and the extraction of characteristic parameters of roughness casting surface. The interface of image preprocessing and the extraction of roughness evaluation parameters is compiled by MA TLAB which can provide a solid foundation for the online and fast detection of casting surface roughness.

REFERENCE

[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction [1]. Optical instruments 1996, 18 (1): 32-37.

[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin: Harbin University of Science and Technology

[3] BRADLEY C. Automated surface roughness measurement[1]. The International Journal of Advanced Manufacturing Technology ,2000,16(9) :668-674.

[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method [J]. Aerospace measurement technology, 2000, 20(4): 2-10.

[5] Liu He. Digital image processing and application [ M]. China Electric Power Press, 2005

译文：

数字图像处理在铸件表面粗糙度测量中的应用

摘要—本文提出了一种表面图像采集基于数字图像处理技术的系统。由CCD获得的图像的步骤是通过预先处理图像编辑，图像均衡，图像二进制对话和特征参数的提取，实现铸件表面粗糙度测量。三维评价方法是得到评价参数和铸件表面粗糙度的特征参数的提取。一种基于MA TLAB的铸造表面粗糙度自动检测接口程序，可以提供一个坚实的基础在线和快速的基于图像处理技术的铸造表面粗糙度检测。

关键词—铸造表面粗糙度测量；图像处理；特征参数

Ⅰ.介绍

如今在质量和加工表面粗糙度的高度增加的需求下，由于如非接触，热点速度快，适用于精度高，抗干扰能力强等的优点，基于图像处理的机器视觉检测已成为机械工业中主要测量技术之一[1,2]。由于没有规定和限制，铸件表面粗糙度的范围是广泛的，检测参数与高度方向光电技术的发展，不能满足目前的要求，水平间距或粗糙度也需要一个定量表示。因此，基于图像处理技术的表面粗糙度测量方法，对铸造表面粗糙度建立三维评价体系为目标[ 3,4 ]。通过图像增强处理，推导出图像的预处理和图像二值谈话。三维粗糙度是基于特征参数进行评价的。一种基于MA TLAB的铸造表面粗糙度自动检测界面的编制提供了坚实的在线快速铸造表面粗糙度检测。

Ⅱ.铸件表面图像采集系统

采集系统由采样载体，显微镜，CCD摄像头，图像采集卡和计算机组成。样品载体是用来测试铸件。根据实验要求，我们可以选择一个固定的载体，采样位置可以手动转换，选择固化试样与采样阶段的位置是可以改变的。图1显示了整个加工过程，首先，检测到铸件应

尽可能放置在明亮的背景下，然后通过调节光学透镜，设置CCD摄像机分辨率和曝光时间，对CCD采集到的图片通过采集卡保存到计算机内存。根据相应的软件对铸件表面进行图像预处理和特征值提取，最后检测结果输出。

图1 铸造图像采集系统

Ⅲ.铸件表面图像处理

铸件表面图像处理主要包括图像编辑，均衡处理，图像增强和图像二值谈话等。原始的图像测量铸件图2中给出。其中（a）显示了原始图像和（b)显示剪辑图像。

A.图像增强

图像增强是一种处理方法，可以突出某些图像信息,根据特定的需要同时可以削弱或删除一些不必要的信息[5]。为了获得更清楚轮廓的铸件表面均匀化处理的图像即校正图像的直方图应在图像分割处理前预先处理。图3显示了原始灰度图像及其直方图均衡化处理的图像。如图所示，每个灰度级的直方图具有基本相同的像素点，灰度均衡化处理后变得更加平。校正后的对比度增强的图像将变得更加清晰。

a) 原始图像 b) 修剪图像

图2 铸件表面图像

a) 灰度图像 b) 直方图

c)均衡图像 d）均衡直方图

图3 均衡处理图像

B.图像分割

图像分割是在本质上的像素分类的过程。它是由阈值分类的一个非常重要的技术。最优阈值是通过instmction脱粒= graythresh（II）达到的。图4显示图像的二进制谈话。图中的黑色区域显示部分的轮廓的灰度值低于阈值（0.43137），而白色区域表示灰度值大于阈值。阴影和阴影在明亮的区域出现可能造成噪音或表面凹陷。

a) 灰度图像 b) 二值图像

图4 图像的二值化

Ⅳ.粗糙度参数提取

为了检测表面粗糙度，需要提取粗糙度特征参数。平均直方图和方差是用来描述表面轮廓纹理尺寸参数。而单位表面的峰面积参数能反映工件的粗糙度水平。峰度参数可以表征垂

直方向和水平方向的粗糙度。因此，本文建立直方图的均值和方差，单位表面的峰面积和陡度作为粗糙度评价参数的铸件三维评价。图像预处理和特征提取的界面是基于MATLAB编制的。图5显示了表面粗糙度的检测接口。图像预处理通过这个软件成功地实现了可裁剪的铸造，其中包括图像滤波，图像增强，图像分割和直方图均衡化，而且还可以显示所提取的评价表面粗糙度参数。

图5自动粗糙度测量接口

V.结论

本文研究了铸件表面粗糙度测量方法的基础上的数字图像处理技术。该方法由图像采集，图像增强，图像二值的对话和铸件表面的粗糙度特征参数的提取组成。MA TLAB编译图像预处理和提取粗糙度评估参数的接口，它可以提供铸件表面粗糙度的在线和快速检测一个

坚实的基础。

参考文献

[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction [1]. Optical instruments 1996, 18 (1): 32-37.

[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin: Harbin University of Science and Technology

[3] BRADLEY C. Automated surface roughness measurement[1]. The International Journal of Advanced Manufacturing Technology ,2000,16(9) :668-674.

[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method [J]. Aerospace measurement technology, 2000, 20(4): 2-10.

[5] Liu He. Digital image processing and application [ M]. China Electric Power Press, 2005