【摘要】：本文基于灰色關(guān)聯(lián)分析可以進(jìn)行邊緣檢測(cè)這一理論依據(jù),研究了基于灰色關(guān)聯(lián)分析的傳統(tǒng)邊緣檢測(cè)算法原理,改進(jìn)了傳統(tǒng)算法中存在的抗噪性能差、閾值設(shè)置主觀性強(qiáng)等缺陷。文中首先介紹了邊緣檢測(cè)的相關(guān)背景知識(shí)以及基于灰色關(guān)聯(lián)分析的傳統(tǒng)邊緣檢測(cè)理論基礎(chǔ)及研究進(jìn)展。其次,針對(duì)傳統(tǒng)算法存在的缺陷進(jìn)行改進(jìn)。加入中值濾波器對(duì)待檢測(cè)圖像進(jìn)行平滑濾波,增強(qiáng)算法的抗噪性能;結(jié)合人眼視覺特性提出了一種自適應(yīng)的計(jì)算閾值的微分方程,該方程由圖像待檢測(cè)像素點(diǎn)周圍3×3鄰域的平均灰度值組成,克服了傳統(tǒng)算法閾值設(shè)定主觀性強(qiáng)的缺陷;對(duì)改進(jìn)算法進(jìn)行仿真分析,通過處理邊緣點(diǎn)的八鄰域區(qū)域點(diǎn),改善了改進(jìn)閾值提取出的邊緣偽邊緣較多現(xiàn)象;實(shí)驗(yàn)仿真證明,改進(jìn)算法對(duì)含有較高濃度的椒鹽噪聲有很好的抑制效果,自適應(yīng)閾值提取出的邊緣較傳統(tǒng)算法定位誤差更小。最后,主要通過實(shí)驗(yàn)仿真,分別從抗噪性能、定位誤差、線性連接程度、邊緣連續(xù)性等方面,將改進(jìn)算法與經(jīng)典微分算子的檢測(cè)性能進(jìn)行對(duì)比。實(shí)驗(yàn)數(shù)據(jù)證明,改進(jìn)算法相比經(jīng)典算法而言檢測(cè)出的邊緣圖像更為完整,邊緣連續(xù)、較細(xì),定位精度較高,對(duì)于含有較高濃度的椒鹽噪聲圖像,也取得了經(jīng)典算法不可比擬的優(yōu)勢(shì)。探討了改進(jìn)算法對(duì)不同類型圖像的適用性以及算法時(shí)間在不同灰度級(jí)圖像中的性能,結(jié)果證明本文算法適用性強(qiáng)。
[Abstract]:Based on the theory that the gray correlation analysis can detect the edge, this paper studies the principle of the traditional edge detection algorithm based on the grey correlation analysis, and improves the shortcomings of the traditional algorithm, such as poor anti-noise performance and strong subjectivity of threshold setting. This paper first introduces the background knowledge of edge detection, the theoretical basis and research progress of traditional edge detection based on grey correlation analysis. Secondly, the defects of the traditional algorithm are improved. The median filter is added to the detection image for smoothing filtering to enhance the anti-noise performance of the algorithm. In this paper, an adaptive differential equation for calculating threshold is proposed based on human visual characteristics. The equation is composed of the average gray value of 3 脳 3 neighborhood around the pixels to be detected in the image, which overcomes the subjective disadvantage of the traditional threshold setting algorithm. The improved algorithm is simulated and analyzed. By dealing with the eight neighborhood region points of the edge points, the phenomenon of more pseudo-edge of the edge extracted by the improved threshold is improved. The experimental results show that the improved algorithm can suppress the salt and pepper noise with high concentration, and the edge extracted by the adaptive threshold is less than the traditional algorithm. Finally, the improved algorithm is compared with the classical differential operator in the aspects of anti-noise performance, location error, linear connection degree, edge continuity and so on. The experimental data show that the edge image detected by the improved algorithm is more complete, the edge is continuous, the edge is finer, and the localization accuracy is higher than the classical algorithm, for the image with high concentration of salt and pepper noise, The advantages of the classical algorithm are also obtained. The applicability of the improved algorithm to different types of images and the performance of the algorithm time in different gray-scale images are discussed. The results show that the proposed algorithm has strong applicability.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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