本文關(guān)鍵詞：基于改進(jìn)馬爾科夫隨機(jī)場(chǎng)與精確高度函數(shù)的列車故障圖像層次特征匹配　出處：《湖北工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： TFDS 層次特征匹配 馬爾科夫隨機(jī)場(chǎng) 精確高度函數(shù) 形狀精度

【摘要】：鐵路列車運(yùn)行故障動(dòng)態(tài)圖像檢測(cè)系統(tǒng)(TFDS)是一套由我國(guó)自主研發(fā)的基于機(jī)器視覺(jué)的列車軌邊故障圖像檢測(cè)系統(tǒng)。針對(duì)TFDS圖像顏色單一和背景復(fù)雜等特點(diǎn),利用圖像的空間層次性及其故障區(qū)域的形狀特征,提出基于改進(jìn)馬爾科夫隨機(jī)場(chǎng)與精確高度函數(shù)的列車故障圖像層次特征匹配算法,將故障圖像識(shí)別分成層次模型建立與形狀匹配兩部分,用以實(shí)現(xiàn)列車常見(jiàn)故障的自動(dòng)檢測(cè)。從圖像像素的空間交互關(guān)系入手,采用馬爾科夫隨機(jī)場(chǎng)(MRF),結(jié)合圖像金字塔與近鄰傳播理論,提出基于快速自適應(yīng)MRF的層次分割算法。首先利用小波變換與MRF理論,建立圖像多尺度表達(dá)的層次模型,接著引入直方圖平滑與近鄰傳播算法自動(dòng)指定模型層次數(shù),并利用改進(jìn)的K-means算法實(shí)現(xiàn)圖像的快速自適應(yīng)分割,最后依據(jù)分割過(guò)程中的能量穩(wěn)定性,將像素標(biāo)準(zhǔn)差微分作為迭代準(zhǔn)則,進(jìn)一步提升運(yùn)算速度與魯棒性。在McGill和Weizmann圖像數(shù)據(jù)庫(kù)上部分圖像的測(cè)試結(jié)果表明,該算法計(jì)算速度較快,分割性能好且略優(yōu)于MRF算法,分割效率較MRF算法提升至少40%。鑒于形狀匹配在圖像識(shí)別中的實(shí)用性,在高度函數(shù)描述子(HF)的基礎(chǔ)上,提出一種精確高度函數(shù)特征(EHF)描述算法。首先構(gòu)造目標(biāo)形狀外輪廓采樣點(diǎn)的EHF描述符并進(jìn)行特征降維,其次利用并行動(dòng)態(tài)規(guī)劃進(jìn)行形狀匹配,最后引入形狀復(fù)雜度分析提升匹配效果�；邳c(diǎn)的幾何特征顯著性,提出形狀精度理論,進(jìn)一步分析局部形變與邊緣噪聲對(duì)形狀特征描述的影響。在MPEG-7、Swedish Leaf、Tools和ETH-80數(shù)據(jù)庫(kù)上進(jìn)行匹配實(shí)驗(yàn)以及在Kimia99數(shù)據(jù)庫(kù)上進(jìn)行抗噪實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明:該算法效率高,匹配時(shí)間僅為HF的12.5%,在上述匹配實(shí)驗(yàn)中的檢索率分別為90.36%、95.07%、94.29%和89.90%,檢索性能優(yōu)于HF和其他重要算法;在抗噪實(shí)驗(yàn)中,該算法的抗噪性能也較HF有明顯提升。依據(jù)TFDS圖像的自身特征,結(jié)合圖像層次特征模型和形狀匹配算法,提出的列車故障圖像層次特征匹配算法,能實(shí)現(xiàn)空氣制動(dòng)系統(tǒng)故障、車輪擋鍵丟失、高摩合成閘瓦丟失和制動(dòng)梁安全鏈脫落四類列檢故障的自動(dòng)識(shí)別,該算法缺陷識(shí)別率高,魯棒性好,能有效地應(yīng)用于TFDS故障圖像檢測(cè)中。
[Abstract]:The dynamic image detection system of railway train running fault (TFDS) is a set of train track fault image detection system based on machine vision, which is developed by our country independently. According to the characteristics of the complex TFDS image of a single color and background, using the shape feature space hierarchy and fault area of the image, put forward improved Markov random field and the exact height function of train fault image level feature matching algorithm based on image recognition, the fault is divided into two parts and establish the hierarchical shape matching model, with automatic detection to achieve common the fault of the train. Starting from the spatial interaction relationship of image pixels, Markov random field (MRF) and image Pyramid and nearest neighbor propagation theory are applied to propose a hierarchical segmentation algorithm based on fast adaptive MRF. Firstly, using wavelet transform and MRF theory, establish the hierarchy model expression of multiscale image, then introduce the histogram smoothing and affinity propagation algorithm to automatically specify the model layer number, segmentation fast adaptive and improved K-means algorithm to realize image segmentation, based on the energy stability in the process of the pixel standard deviation differential as the iterative criterion, further to improve computing speed and robustness. The test results of some images on McGill and Weizmann image databases show that the algorithm is fast and has better segmentation performance and slightly better than MRF algorithm, and the segmentation efficiency is improved by at least 40% compared with MRF algorithm. In view of the practicality of shape matching in image recognition, an exact height function feature (EHF) description algorithm is proposed on the basis of the height function descriptor (HF). First, we construct the EHF descriptor of the target contour and sampling points, and feature reduction. Secondly, we use parallel dynamic programming for shape matching. Finally, we introduce the shape complexity analysis to improve the matching effect. Based on the geometric feature saliency of point, the shape precision theory is proposed to further analyze the influence of local deformation and edge noise on the description of shape feature. In MPEG-7, Swedish, Leaf Tools and ETH-80 database, and the anti noise experiments on the Kimia99 database, the experimental results show that the proposed algorithm is of high efficiency, matching time is only 12.5% of HF, in the matching experiment in retrieval rate were 90.36%, 95.07%, 94.29% and 89.90%, and the retrieval performance is better than HF other important algorithm; the anti noise experiment, the anti noise performance of the algorithm is HF has improved significantly. According to the characteristics of TFDS image, and image level feature model and shape matching algorithm, put forward the train fault image level feature matching algorithm, can realize the air brake system failure, wheel gear key loss, high friction composite brake shoe and brake beam safety chain lost off four class train inspection fault automatic recognition algorithm, the recognition rate of defects high, good robustness, can be effectively applied to the fault detection in TFDS image.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U279.3;TP391.41

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本文編號(hào)：1345787