外文翻译---基于机器视觉数字图像放大应用于表面粗糙度的评估(编辑修改稿)

外文翻译---基于机器视觉数字图像放大应用于表面粗糙度的评估(编辑修改稿)内容摘要：

ylus profil ometer measurements of average roughness (area) per formed on the same surface. Luk et al. [1] utilized statistical parameters, derived from the grey level intensity histogram such as the range and the mean value of the distribution and correlated them with the Ravalue determined from the stylus method. AlKindi et al. [2] implemented a technique utilizing a roughness parameter based on both the spacing between grey level peaks and the number of grey level peaks per unit length of a scanned line in the grey level image to estimate the surface roughness. DuMing Tsai et al. [3] employed a twodimensional Fourier transform of a cast surface in both the grey level image and binary image to estimate the surface roughness of castings (for surfaces with RaO10 mm). Jason et al. [4] first scan the scattering pattern from the surface, the analog and digital electronics measure the light intensity incident on each detector, then pute both the reflected and total incident intensity which are then used to pute the surface roughness (for surfaces with RaO20 mm). Bradley et al. [5] employed a fiber optics sensor for surface roughness measurement. In their work, changes in the surface topography are manifested as phase R. Kumar et al. / International Journal of Machine Tools amp。 Manufacture 45 (2020) 228–234 229 changes of the incident and reflected light on the surface. Hisayoshi Sato et al. [6] worked on the estimation of surface roughness using a scanning electron microscope. They showed that the profile of a surface could be obtained by processing back scattered electron signals which are in proportion to the surface inclination along the electron beam scanning, which meant that the profile of the surface roughness can be derived by integrating the intensity of the back scattered electron signal. Bjuggern et al. [7] developed a total integrated infrared scatterometer to perform the rms roughness measurements of engineering surfaces. Hasegawa et al. [8] employed fractal characteristics of the ARMA model in an approach to model a machined surface profiles. Carneiro et al. [9] measured the surface roughness using scanning probe microscopy, which includes more than 20 threedimensional roughness parameters to characterize the surface topography. After capturing the images of surfaces using machine vision systems manufactured by various processes including shaping, milling, grinding, etc. Ramamoorthy et al. [10,11] have utilized the grey level intensity histograms, etc. for establishing new optical parameters for roughness evaluation. Ramamoorthy et al. [12] have also used stereometry techniques to get the three dimensional depth profiles of such surfaces and successfully estimated the surface area and volume of the ponents. Most state of the art digital image magnification techniques suffer from the limitation that they do not introduce any new information to the original image. This lack of information, more precisely the absence of high spatial frequency ponents is responsible for the perceptible degradation of magnified images, which are reflected, in blurred edges. Interpolation methods are usually employed in magnification of digital images. One of the best interpolation schemes namely cubic convolution developed by Keys [13] approximates the ideal sinc function by truncating it and this nonideal interpolation cuts some high frequencies, which are present in the original image, leading to band limiting effects on the high resolution image. Although the cubic spline method generates a better highresolution version of an image, putationally it is much more cumbersome pared to cubic convolution. Edge blurring is even more severe with other magnification techniques. There have been several attempts in the past for improvements to achieve image magnification. Hewlett Packard [14] has reported an approach in this regard which is patented by them. Most of these methods use edge information at the low resolution of the original image to be interpolated. Allebach and Wong [15] use a subpixel edge estimation technique to generate a high resolution edge map from the low resolution image, and then use the high resolution edge map to guide the interpolation of the low resolution image to the final high resolution version. Jensen and Anastassiou [16] present an approach for resolution enhancement based on a new edge fitting operator. A small neighborhood of 3!3 about each pixel in the lowresolution image is first mapped to a best fit continuous space step edge. The bilevel approximation serves as a local template on which the higher resolution sampling grid can then be superimposed (where disputed values in regions of local window overlap are averaged to smooth errors). The result is an image of increased resolution with noticeably sharper edges. Biancardi et al. [17] estimate the phases and frequencies of absent wave forms of absent frequencies from the original low resolution image and then synthesize them in the high resolution image. This technique, like the one by Allebach and Wong, takes advantage of subpixel edge estimation from the low resolution image to direct the subsequent polynomial interpolation step. There are limitations even in the widely accepted mechanical stylus methods such as evaluation of roughness, waviness and form error. Various electrical filters, cut off ratios and magnification are used during evaluation. Here in this work an attempt is made to digitally magnify the surface image. To test the quantification parameters evaluated using this method, a parative study has been presented with the mechanical stylus parameters with plete analysis. It has been finally established that this digital magnification followed by qualitative evaluation of surface images could be very well used for engineering sur。