本文選題：同步碎石封層 + 顆粒圖像幾何畸變�。� 參考：《鄭州大學》2015年碩士論文

【摘要】：同步碎石封層施工是一種先進的公路施工工藝,石子撒布覆蓋率是同步碎石封層施工的一項重要指標。目前的石子撒布率檢測方法依靠人眼觀察或計數(shù),工作量大、效率低、受到主觀因素影響,并且難以融入自動化施工管理系統(tǒng)。而基于圖像的工控和檢測技術正在迅速發(fā)展,為了能夠應用數(shù)字圖像處理技術解決石子撒布率檢測的問題,本文對有關算法進行了研究。本文首先研究了為實現(xiàn)石子撒布率檢測系統(tǒng)所必須的圖像預處理算法,主要包括圖像幾何畸變恢復和圖像增強,接著研究并實驗了多種形態(tài)學圖像處理技術在施工石子顆粒圖像的應用,最后,本文提出一種采用分水嶺算法和區(qū)域形態(tài)學特征,對顆粒圖像進行分割與合并,并對顆粒進行計數(shù)的算法。本文的主要工作可概括為以下幾個方面:1、為了恢復鏡頭的幾何畸變的圖像,往往根據(jù)幾何模型重新計算像素位置并采用雙線性插值,由于一幅圖像中不同位置的畸變程度不同(中間部分畸變較小,邊緣部分畸變強烈),插值造成的圖像模糊程度也不相同。本文根據(jù)其頻域響應的分析,在圖像的不同位置采用不同的圖像增強參數(shù),改善了上述問題。2、本文第二部分重點研究了形態(tài)學圖像處理的算法,包括區(qū)域生長算法,基于標記的分水嶺算法,以及基于距離變換(distance transform)的分水嶺算法。通過實驗可以比較出這些算法處理石子顆粒圖像的效果。3、針對粘連嚴重、分辨率和清晰度較低的顆粒圖像分割和計數(shù)的問題,本文提出了一種基于分水嶺和形態(tài)學特征的方法。首先使用分水嶺算法分割圖像,得到過分割的結果,接著通過本文定義的區(qū)域形態(tài)學特征,采用加權馬氏距離和區(qū)域連接圖(region adjacency graph)指導過分割區(qū)域的合并。還討論了加權馬氏距離的性質,以及一些在不影響效果的情況下減少運算量的機制。通過實驗表明,該算法效果良好,對原始圖像的灰度、對比度和噪聲變化具有不變性,在查全率-準確率(precision-recall)曲線的表現(xiàn)上優(yōu)于現(xiàn)有方法。
[Abstract]:Synchronous gravel sealing construction is an advanced highway construction technology, and the coverage rate of stone distribution is an important index of synchronous gravel sealing construction. The present method for detecting the rate of stone distribution depends on the observation or counting of human eyes, which has the advantages of heavy workload, low efficiency, subjective factors, and difficult to be integrated into the automatic construction management system. But the technology of industrial control and detection based on image is developing rapidly. In order to solve the problem of detecting the distribution rate of stone by using digital image processing technology, the relevant algorithms are studied in this paper. In this paper, we first study the image preprocessing algorithm which is necessary to realize the detection system of stone distribution rate, which includes image geometric distortion recovery and image enhancement. Then we study and experiment the application of various morphological image processing techniques in the construction of stone particle image. Finally, this paper proposes a watershed algorithm and regional morphological features to segment and merge the particle image. And the algorithm of counting the particles. The main work of this paper can be summarized as follows: 1. In order to restore the geometric distortion image of the lens, the pixel position is often recalculated according to the geometric model and bilinear interpolation is used. Because the distortion degree of different positions in an image is different (the middle part distortion is small, the edge part distortion is strong), the image ambiguity degree caused by interpolation is also different. In this paper, according to the analysis of frequency domain response, different image enhancement parameters are adopted in different positions of the image to improve the above problem. The second part of this paper focuses on the morphological image processing algorithm, including the region growth algorithm. The watershed algorithm based on label and the watershed algorithm based on distance transform. The results of these algorithms can be compared by experiments. Aiming at the problem of segmentation and counting of grained images with serious adhesion and low resolution and sharpness, a method based on watershed and morphological features is proposed in this paper. Firstly, the watershed algorithm is used to segment the image, and the result of over-segmentation is obtained. Then, the weighted Markov distance and the region adjacency are used to guide the merging of the over-segmented regions through the morphological features of the region defined in this paper. The properties of weighted Markov distance and some mechanisms for reducing computation without affecting the effect are also discussed. The experimental results show that the algorithm is effective and invariant to the gray, contrast and noise changes of the original image, and is superior to the existing methods in the performance of recall-accuracy precision-recalline curve.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U416.2;TP391.41

【共引文獻】

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