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1、外文文献翻译 (含：英文原文及中文译文) 文献出处： Tian, X., Wang, X., Wang, L., & Dong, H. (2012). Application of digital image processing in the measurement of casting surface roughness.International Conference on Computer Science and Information Processing(pp.19-21). IEEE.英文原文 Application Of Digital Image Processing In
2、The Measurement Of Casting Surface RoughnessTian, X., Wang, X., Wang, L., & Dong, HAhstract 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
3、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 interfac
4、e 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 INTRODUCTIONNowad
5、ays 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,
6、 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 rough
7、ness 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 enha
8、ncement 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
9、of casting surface roughness.II. CASTING SURFACE IMAGE ACQUISITION SYSTEMThe 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 sele
10、ct 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, a
11、nd 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.
12、 Finally the detecting result is output.III. CASTING SURFACE IMAGE PROCESSINGCasting 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 whi
13、ch a) presents the original image and b) shows the clipped image.A. Image EnhancementImage 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 time5.In order to obtain mo
14、re 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 th
15、e 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.B. Image SegmentationImage segmentation is the process of pixel cl
16、assification 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 c
17、ontour 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.IV . ROUGHNESS PARAMETER EXTRACTIONIn order to detect the surface roughness, it is necessar
18、y to extract feature parameters of roughness. The average histogram and variance are parameters used to characterize the texture size of surface contour. While unit surfaces peak area is parameter that can reflect the roughness of horizontal workpiece. And kurtosis parameter can both characterize th
19、e roughness of vertical direction and horizontal direction. Therefore, this paper establishes histogram of the mean and variance, the unit surfaces peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface i
20、s 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
21、 the extracted evaluation parameters of surface roughness.V . CONCLUSIONSThis 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 extracti
22、on 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.中文译文数字图像处理在铸件表面粗糙度测量中的应用
23、Tian, X., Wang, X., Wang, L., & Dong, H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1引言目前对加工质量和表面粗糙度的要求大大提高，基于图像处理的机器视觉检测由于具有非接触，速度快，适用
24、性强等优点已成为机械工业测量技术的热点之一精度高，抗干扰能力强等优点1,2。由于没有关于铸件表面的规律，而且粗糙度范围很宽，与高度方向有关的检测参数不能满足当前光电技术发展的要求，水平间距或粗糙度也需要定量表示。因此，建立了铸件表面粗糙度三维评价体系作为目标3,4，提出了基于图像处理技术的表面粗糙度测量方法。图像预处理是通过图像增强处理，图像二进制对话推导出来的。执行基于特征参数的三维粗糙度评估。编制了基于MA TLAB的铸件表面粗糙度自动检测界面，为在线快速检测铸件表面粗糙度提供了坚实的基础。2铸造表面图像采集系统采集系统由样品载体，显微镜，CCD摄像头，图像采集卡和计算机组成。样品载体用于
25、放置测试铸件。根据实验要求，我们可以选择一个固定的载体，可以手动转换样品位置，或者选择固化标本，并可以改变采样阶段的位置。图1显示了整个处理过程。首先，检测到的铸件应尽可能放置在照明背景中，然后通过调节光学镜头，设置CCD摄像机的分辨率和曝光时间，将CCD采集的图像保存到计算机内存通过采集卡。其次是基于相应的软件对铸件表面进行图像预处理和特征值提取。最后输出检测结果。3铸造表面图像处理铸造表面图像处理包括图像编辑，均衡处理，图像增强和图像二进制对话等。被测铸件的原始图像和剪切图像如图2所示。其中a）显示原始图像，b）显示剪切图像。A.图像增强图像增强是一种处理方法，它可以根据某些特定的需要来突
26、出某些图像信息，同时削弱或去除一些不需要的信息5。为了获得更加清晰的图像的表面均衡处理的轮廓即在图像分割处理之前应该对图像直方图的校正进行预处理。图3显示了原始灰度图像和均衡处理图像及其直方图。如图所示，直方图的每个灰度级具有基本上相同的像素点，并在灰度均衡处理后变得更平坦。校正后图像显得更清晰，图像的对比度增强。B.图像分割图像分割本质上是像素分类的过程。这是一个非常重要的技术，通过阈值分类。最佳阈值是通过安全阈值= graythresh（II）获得的。图4显示了二进制对话的图像。图像黑色区域的灰度值显示轮廓部分小于阈值（0.43137），而白色区域显示大于阈值的灰度值。明亮区域出现的阴影和
27、阴影可能由噪音或表面凹陷引起。4粗糙度参数提取为了检测表面粗糙度，有必要提取粗糙度的特征参数。平均直方图和方差是用于表征表面轮廓的纹理大小的参数。单位表面的峰值面积是反映水平工件粗糙度的参数。峰度参数可以表征垂直方向和水平方向的粗糙度。因此，本文建立了均值和方差的直方图，单位表面的峰面积和陡度作为铸件三维评估的粗糙度评估参数。基于MATLAB编写图像预处理和特征提取界面。图5显示了表面粗糙度的检测界面。该软件可以成功实现图像预处理，包括图像滤波，图像增强，图像分割和直方图均衡等处理，还可以显示提取的表面粗糙度评估参数。5结论本文研究了基于数字图像处理技术的铸件表面粗糙度测量方法。该方法由图像采集，图像增强，图像二元对话和粗糙度铸造表面特征参数的提取组成。 MA TLAB编制图像预处理界面和粗糙度评估参数提取界面，为在线快速检测铸件表面粗糙度提供了坚实的基础。