【正文】 tially the same pixel point and bees more flat after gray equalization processing. The image appears more clearly after the correction and the contrast of the image is enhanced. Casting surface image Equalization processing imageB. Image SegmentationImage 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 (), 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 conversationIV. ROUGHNESS PARAMETER EXTRACTIONIn 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 surface39。s peak area is parameter that can reflect the roughness of horizontal 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 surface39。s peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface is piled 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. Automatic roughness measurement interfaceV. CONCLUSIONSThis paper investigates the casting surface roughness measuring method based on digital Image processing technology. The method is posed 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 piled 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): 3237.[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) :668674.[4] Li Chenggui, Li xingshan, Qiang XIFU 3D surface topography measurement method [J]. Aerospace measurement technology, 2000, 20(4): 210.[5] Liu He. Digital image processing and application [ M]. China Electric Power Press, 2005數(shù)字圖像處理在鑄件表面粗糙度測量中的應(yīng)用Tian Xiaojing, Wang Xiaoyu, Wang Longji 大連交通大學，遼寧，中國，tzy@Dong Huajun 1大連交通大學，遼寧，中國，tzy@ ，河南，中國， huajundong4025@摘要—本文提出了一種表面圖像采集基于數(shù)字圖像處理技術(shù)的系統(tǒng)。由CCD獲得的圖像的步驟是通過預(yù)先處理圖像編輯，圖像均衡，圖像二進制對話和特征參數(shù)的提取，實現(xiàn)鑄件表面粗糙度測量。三維評價方法是得到評價參數(shù)和鑄件表面粗糙度的特征參數(shù)的提取。一種基于MA TLAB的鑄造表面粗糙度自動檢測接口程序，可以提供一個堅實的基礎(chǔ)在線和快速的基于圖像處理技術(shù)的鑄造表面粗糙度檢測。關(guān)鍵詞—鑄造表面粗糙度測量；圖像處理；特征參數(shù)Ⅰ.介紹如今在質(zhì)量和加工表面粗糙度的高度增加的需求下，由于如非接觸，熱點速度快，適用于精度高，抗干擾能力強等的優(yōu)點，基于圖像處理的機器視覺檢測已成為機械工業(yè)中主要測量技術(shù)之一[1,2]。由于沒有規(guī)定和限制，鑄件表面粗糙度的范圍是廣泛的，檢測參數(shù)與高度方向光電技術(shù)的發(fā)展，不能滿足目前的要求，水平間距或粗糙度也需要一個定量表示。因此，基于圖像處理技術(shù)的表面粗糙度測量方法，對鑄造表面粗糙度建立三維評價體系為目標[ 3,4 ]。通過圖像增強處理，推導出圖像的預(yù)處理和圖像二值談話。三維粗糙度是基于特征參數(shù)進行評價的。一種基于MA TLAB的鑄造表面粗糙度自動檢測界面的編制提供了堅實的在線快速鑄造表面粗糙度檢測。Ⅱ.鑄件表面圖像采集系統(tǒng)采集系統(tǒng)由采樣載體，顯微鏡，CCD攝像頭，圖像采集卡和計算機組成。樣品載體是用來測試鑄件。根據(jù)實驗要求，我們可以選擇一個固定的載體，采樣位置可以手動轉(zhuǎn)換，選擇固化試樣與采樣階段的位置是可以改變的。圖1顯示了整個加工過程，首先，檢測到鑄件應(yīng)盡可能放置在明亮的背景下，然后通過調(diào)節(jié)光學透鏡，設(shè)置CCD攝像機分辨率和曝光時間，對CCD采集到的圖片通過采集卡保存到計算機內(nèi)存。根據(jù)相應(yīng)的軟件對鑄件表面進行圖像預(yù)處理和特征值提取，最后檢測結(jié)果輸出。 圖1 鑄造圖像采集系統(tǒng)Ⅲ.鑄件表面圖像處理鑄件表面圖像處理主要包括圖像編輯，均衡處理，圖像增強和圖像二值談話等。原始的圖像測量鑄件圖2中給出。其中（a）顯示了原始圖像和（b)顯示剪輯圖像。A. 圖像增強圖像增強是一種處理方法，可以突出某些圖像信息,根據(jù)特定的需要同時可以削弱或刪除一些不必要的信息[5]。為了獲得更清楚輪廓的鑄件表面均勻化處理的圖像即校正圖像的直方圖應(yīng)在圖像分割處理前預(yù)先處理。圖3顯示了原始灰度圖像及其直方圖均衡化處理的圖像。如圖所示，每個灰度級的直方圖具有基本相同的像素點，灰度均衡化處理后變得更加平。校正后的對比度增強的圖像將變得更加清晰。 a) 原始圖像 b) 修剪圖像 圖2 鑄件表面圖像 a) 灰度圖像 b) 直方圖 c)均衡圖像 d）均衡直方圖 圖3 均衡處理圖像圖像分割是在本質(zhì)上的像素分類的過程。它是由閾值分類的一個非常重要的技術(shù)。最優(yōu)閾值是通過instmction脫粒= graythresh（II）達到的。圖4顯示圖像的二進制談話。圖中的黑色區(qū)域顯示部分的輪廓的灰度值低于閾值（），而白色區(qū)域表示灰度值大于閾值。陰影和陰影在明亮的區(qū)域出現(xiàn)可能造成噪音或表面凹陷。 a) 灰度圖像 b) 二值圖像 圖4 圖像的二值化Ⅳ.粗糙度參數(shù)提取 為了檢測表面粗糙度，需要提取粗糙度特征參數(shù)。平均直方圖和方差是用來描述表面輪廓紋理尺寸參數(shù)。而單位表面的峰面積參數(shù)能反映工件的粗糙度水平。峰度參數(shù)可以表征垂直方向和水平方向的粗糙度。因此，本文建立直方圖的均值和方差，單位表面的峰面積和陡度作為粗糙度評價參數(shù)的鑄件三維評價。圖像預(yù)處理和特征提取的界面是基于MATLAB編制的。圖5顯示了表面粗糙度的檢測接口。圖像預(yù)處理通過這個軟件成功地實現(xiàn)了可裁剪的鑄造，其中包括圖像濾波，圖像增強，圖像分割和直方圖均衡化，而且還可以顯示所提取的評價表面粗糙度參數(shù)。 圖5自動粗糙度測量接口本文研究了鑄件表面粗糙度測量方法的基礎(chǔ)上的數(shù)字圖像處理技術(shù)。該方法由圖像采集，圖像增強，圖像二值的對話和鑄件表面的粗糙度特征參數(shù)的提取組成。MA TLAB編譯圖像預(yù)處理和提取粗糙度評估參數(shù)的接口，它可以提供鑄件表面粗糙度的在線和快速檢測一個堅實的基礎(chǔ)。參考文獻[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction [1]. Optical instruments 1996, 18 (1): 3237.[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) :668674.[4] Li Chenggui, Li xingshan, Qiang XIFU 3D surface topography measurement method [J]. Aerospace measurement technology, 2000, 20(4): 210.[5] Liu He. Digital image processing and application [ M]. China Electric Power Press, 2005第 23 頁 共 23 頁