本文選題：局部二值模式 + 特征提取��； 參考：《西南交通大學》2017年碩士論文

【摘要】：紋理特征是圖像的重要底層特征之一,本文對由表示圖像紋理特征的紋理譜方法演變而來的LBP算法進行研究,并將改進的算法應用于圖像的分類識別,目標追蹤和圖像分割當中。本文主要工作如下:1、研究LBP模式分類方法中的等價模式和旋轉不變的等價模式,提出一種新的模式分類方式即按照0/1變換次數(shù)和二進制碼值中1的數(shù)目進行分類。通過圖像直方圖和常用紋理庫的對比試驗可以看出本文提出的模式分類方法具有較高的紋理識別能力。2、用生物學中的共生概念對圖像處理中的一些方法進行分析和解釋,然后按照共生概念對這些圖像處理方法進行分類。針對成對旋轉不變的局部二值模式算法(PRICoLBP)提取方法提取的紋理特征計算復雜度高、旋轉不變性較差、對較小的紋理結構特征不敏感的缺陷,提出一種改進的PRICoLBP算法。首先,改進原有算法對共生點對的選取方式,使得改進算法在保持統(tǒng)計更高階紋理信息能力的同時,又增強了圖像對旋轉變化和光照條件變化的魯棒性。其次,該算法融合了灰度值大小關系特征和灰度值差值幅值特征相比于原有算法只提取灰度值大小關系特征能夠提取更多的紋理特征信息,從而提高了算法對較小紋理結構圖像的識別能力。此外,改進算法相比于原有算法的計算維度更小。在對Brodatz,Outex,CUReT和KTH_TIPS圖像紋理庫的分類實驗中,改進算法的識別能力相對于原有算法分別提高了 0.17%,0.24%,2.39%和2.04%。實驗結果表明,改進算法在處理較小紋理結構的圖像時具有較好的識別效果。針對局部二值模式特征(Local Binary Pattern,LBP)對噪聲敏感、旋轉不變性較差的問題,提出一種基于共生的抗噪局部二值模式紋理分類算法。首先,對LBP模式進行重新分類,對等價模式和旋轉不變的等價模式進行擴展;其次,利用共生方法將原圖中表示視覺微觀紋理信息的LBP特征和降采樣后圖像中表示非視覺微觀紋理信息的LBC特征進行并聯(lián),添加圖像的梯度幅值信息,得到一種具有旋轉穩(wěn)定性和抗噪性的紋理特征表示方法。最后,在不同紋理圖像庫中比較本文方法和其他特征表示方法識別率的差別。實驗結果表明,本文方法具有較好的旋轉不變性和抗噪性。3、針對追蹤過程中目標出現(xiàn)遮擋、目標的尺度發(fā)生變化時,STC算法容易丟失追蹤目標的問題,提出一種融合LBP紋理特征的時空上下文追蹤方法。首先,計算每一幀中包含目標區(qū)域的LBP紋理直方圖。其次,利用卡方統(tǒng)計計算第一幀的LBP紋理直方圖與當前幀圖像內目標區(qū)域的LBP紋理直方圖的相似度、相鄰兩幀的目標區(qū)域的LBP紋理直方圖的相似度,當相似度大于設定閾值時認為目標發(fā)生遮擋,改變時空上下文更新系數(shù)和追蹤目標的中心位置坐標。實驗結果表明,該算法相比于原有STC算法在目標被遮擋、目標形狀和尺度發(fā)生變化時均能穩(wěn)健地跟蹤目標,并且能夠保證實時性的要求。4、針對RSF活動輪廓模型對初始輪廓敏感和分割效率較低的問題,提出了一種融合LBP紋理特征的RSF活動輪廓模型。通過引入紋理能量項,使得RSF模型對初始輪廓具有一定的魯棒性和較快的分割速度。
[Abstract]:Texture features are one of the most important features of the image. This paper studies the LBP algorithm which is evolved from the texture spectrum method that represents the texture features of the image, and applies the improved algorithm to image classification, target tracking and image segmentation. The main work of this paper is as follows: 1, the equivalent pattern in the LBP pattern classification method is studied. A new pattern classification method is proposed, which is classified according to the number of 0/1 Transformation Times and the number of 1 in the binary code value. Through the contrast test of the image histogram and the common grain library, it can be seen that the model classification method proposed in this paper has a higher pattern recognition ability.2, and the symbiotic probability in biology is used. Some methods in image processing are analyzed and explained, and then the image processing methods are classified according to the concept of symbiosis. The texture features extracted by the local two value mode algorithm (PRICoLBP) extraction method for the pair rotation invariant are high in complexity, poor in rotation invariance and insensitive to the smaller texture features. An improved PRICoLBP algorithm is proposed. Firstly, improving the selection of the symbiotic point pairs by the original algorithm makes the improved algorithm not only keep the statistical higher order of texture information, but also enhance the robustness of the image to the change of rotation and illumination. Secondly, the algorithm combines the characteristics and gray scale of the size of the gray value. The value difference amplitude feature can extract more texture features compared to the original algorithm only extracting the relationship between the size of gray value, thus improving the recognition ability of the algorithm for the smaller texture image. In addition, the improved algorithm is smaller than the original algorithm. In the Brodatz, Outex, CUReT and KTH_TIPS image grain library, In the classification experiment, the recognition ability of the improved algorithm is improved by 0.17%, 0.24%, 2.39% and 2.04%. respectively. The results show that the improved algorithm has a better recognition effect when dealing with the smaller texture image. The local two value pattern features (Local Binary Pattern, LBP) are sensitive to noise, and the rotation invariance is poor. A kind of symbiotic denoising local two value pattern classification algorithm based on the symbiosis is proposed. First, the LBP pattern is reclassified and the equivalent mode and the equivalent pattern of rotation invariant are extended. Secondly, the LBP features of the visual microscopic texture information are expressed in the original image and the non visual micrograph is expressed in the reduced sample image by the symbiotic method. The LBC features of the texture information are parallel, and the gradient information of the image is added to obtain a texture feature representation method with rotation stability and noise resistance. Finally, the difference of recognition rate between this method and other feature representation methods is compared in different texture images. The results show that the method has good rotation. For invariance and anti noise.3, the STC algorithm is easy to lose the tracking target when the target appears to be obscured and the target scale changes. A spatio-temporal context tracking method which combines the LBP texture features is proposed. First, the LBP texture histogram of the target area is calculated in each frame. Secondly, the Chi square statistics calculation is used. The similarity between the LBP texture histogram of the first frame and the LBP texture histogram of the target area in the current frame image, the similarity degree of the LBP texture histogram of the target area adjacent to two frames. When the similarity is greater than the setting threshold, it is considered that the target is blocked, the time and space context update coefficient and the center position coordinate of the tracking target are changed. It shows that, compared with the original STC algorithm, the target can be steadily tracked when the target is blocked, the shape and scale of the target are changed, and the requirement of real-time is.4. In view of the problem that the RSF active contour model is sensitive to the initial contour and the efficiency of the segmentation is low, a RSF active contour model which combines the LBP texture features is proposed. By introducing texture energy terms, the RSF model has robustness and fast segmentation speed to the initial contour.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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