發(fā)布時(shí)間：2017-12-28 21:08

  本文關(guān)鍵詞：基于機(jī)器視覺(jué)測(cè)量的齒輪圖像邊界提取算法研究　出處：《沈陽(yáng)工業(yè)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 機(jī)器視覺(jué) 邊緣提取 八鄰域 亞像素

【摘要】：齒輪是傳遞運(yùn)動(dòng)和動(dòng)力的基本組成部分,它的制造精度直接決定了其工作狀況的好壞。于是,齒輪的測(cè)量工作便成為研究與生產(chǎn)齒輪的過(guò)程中極為關(guān)鍵的環(huán)節(jié)。傳統(tǒng)的接觸式齒輪測(cè)量方法具有精度低、工作量繁重等弊端,于是出現(xiàn)了基于機(jī)器視覺(jué)的測(cè)量方法。其中圖像的邊緣提取是后續(xù)圖像處理、求取齒輪參數(shù)的前提,所以,本文以基于機(jī)器視覺(jué)的齒輪尺寸測(cè)量為研究背景,以機(jī)器視覺(jué)技術(shù)和圖像處理技術(shù)為理論依據(jù),提出了針對(duì)背光源直齒圓柱齒輪的圖像邊緣提取算法,課題的主要工作如下:第一,論述了齒輪精密測(cè)量的重要性,以及機(jī)器視覺(jué)技術(shù)在齒輪檢測(cè)中的可行性與必要性,而齒輪圖像的邊緣檢測(cè)又是齒輪測(cè)量的必要前提,于是,通過(guò)對(duì)邊緣的研究可知,理想邊緣主要包括階躍型和屋脊型兩種。然后對(duì)齒輪圖像的邊緣類(lèi)型進(jìn)行分析。第二,對(duì)幾種經(jīng)典的像素級(jí)邊緣檢測(cè)算法進(jìn)行了研究,并將這些算子作為對(duì)比實(shí)驗(yàn)。通過(guò)對(duì)實(shí)驗(yàn)結(jié)果結(jié)合理論基礎(chǔ)的研究,總結(jié)這些算法的優(yōu)缺點(diǎn)�；谝陨戏治�,提出基于八鄰域搜索的像素級(jí)邊緣提取算法,以像素八鄰域的位置關(guān)系為基礎(chǔ),通過(guò)高斯濾波對(duì)圖像進(jìn)行平滑處理,根據(jù)比較目標(biāo)像素與八鄰域像素灰度值的大小關(guān)系實(shí)現(xiàn)像素級(jí)邊緣的提取。第三,研究了現(xiàn)有的亞像素級(jí)邊緣提取算法,包括擬合法、插值法、矩法。通過(guò)對(duì)現(xiàn)有亞像素級(jí)邊緣提取算法的研究,提出基于雙線(xiàn)性插值與高斯曲線(xiàn)擬合相結(jié)合的亞像素級(jí)邊緣提取算法,實(shí)驗(yàn)表明本文提出的算法不但保證了邊緣精度,還減少了運(yùn)算時(shí)間。
[Abstract]:Gear is the basic component of transmission motion and power, and its manufacturing precision directly determines its working condition. Therefore, the measurement of gear has become a key link in the study and production of gear. The traditional contact gear measurement method has the disadvantages of low precision and heavy workload, so the measurement method based on machine vision appears. So the image edge extraction is the premise for the subsequent image processing, and take the gear parameters, based on the measurement of gear size based on machine vision as the research background, the machine vision technology and image processing technology as the theoretical basis of image edge extraction algorithm is proposed for the backlight of spur gear, the main subject of the work as follows: first, discusses the importance and feasibility of the gear precision measurement, machine vision technology in detection of gear and gear and the necessity of image edge detection is the necessary premise, gear measurement result, through the research to the edge of the ideal edge including step and roof two. Then the type of the edge of the gear image is analyzed. Second, several classical pixel level edge detection algorithms are studied, and these operators are used as contrast experiments. The advantages and disadvantages of these algorithms are summarized through the research on the theoretical basis of the experimental results combined with the theoretical basis. Based on the above analysis, put forward eight pixel edge extraction algorithm based on neighborhood search, in position between the eight pixel neighborhood based, through the Gauss filter to smooth the image, according to the relationship between pixel size extraction target pixel and neighborhood values to achieve eight pixel edge. Third, the existing sub pixel edge extraction algorithms are studied, including the fitting method, the interpolation method and the moment method. Through the research of the existing sub-pixel edge extraction algorithm, a sub-pixel edge extraction algorithm based on bilinear interpolation and Gauss curve fitting is proposed. Experiments show that the algorithm proposed in this paper not only guarantees the edge accuracy, but also reduces the computation time.
【學(xué)位授予單位】：沈陽(yáng)工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TH132.41;TP391.41

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