本文選題：網格分割 + 網格譜聚類��； 參考：《吉林大學》2016年博士論文

【摘要】：得益于計算機科學和相關數學理論的進步與完善,圖形圖像處理已成為當今非�；钴S的研究方向之一。其中,圖形圖像分割問題一直是該領域中的重要研究課題。經過多年的研究與發(fā)展,圖形圖像分割技術已被廣泛應用于計算機動畫、醫(yī)學影像處理、虛擬現(xiàn)實、計算可視化等多個領域。在許多圖像處理工作中,都需要對圖像中的某些區(qū)域進行提取,我們可以借助圖像分割技術對像素進行劃分,將目標區(qū)域從背景中分離出來。由于圖像分割的結果對后續(xù)的視覺任務有直接影響,這使得圖像分割成為從底層圖像處理進入到圖像識別與理解的關鍵步驟。人類可以準確的將圖像中的目標區(qū)域分離出來,但對于計算機這卻并不是一件容易的事情。多數情況下受圖像自身質量、以及圖像內容的復雜性和多樣性等因素的影響,使計算機很難按照人的理解對圖像進行分割。目前的圖像分割方法多是以圖像中各區(qū)域的相似性,或特征差異度作為判斷準則,將圖像分成互不相交的若干區(qū)域,卻很少把人類視覺特性應用于分割的過程中,致使產生的分割結果通常與人的視覺感知相差甚遠。因此,如何將視覺特性與圖像分割技術相結合,產生更符合人類視覺感知的分割結果,仍然是圖像處理及相關領域中值得深入研究的課題。隨著數據獲取設備的進步以及建模技術的不斷發(fā)展,三維圖形數據已經成為一種新的數字媒體表示形式,對于三維模型的分析與處理也成為計算機圖形學領域的研究熱點。與圖像分割問題一樣,人們仍然希望可以借助人類視覺特性及相關理論對網格模型進行“有意義”的分割,得到多個具有視覺意義或物理意義的部件,以便于從更高層次上對模型進行理解。但通常情況下,人們對于“有意義”部件的定義是非常主觀的,而且在不同的應用背景下,對于“有意義”分割的定義也有所差異。此外與二維圖像數據相比較,三維模型除了幾何屬性外,還包含復雜的空間信息與拓撲信息,這使得三維模型的分割問題更具挑戰(zhàn)性。因此,如何利用人類視覺特性,產生更加符合視覺感知的網格分割結果仍是值得進一步研究的課題。鑒于如何產生更加符合視覺感知的分割結果是圖像分割與三維模型分割共同關注的問題之一,本文從人類視覺特性的角度出發(fā),對圖像分割與網格分割問題進行研究,并分別提出新的圖像分割算法與網格分割算法。本文主要研究工作包括以下幾點：(1)提出一種符合人類視覺特性的圖像自適應閾值分割方法(Visual consistent adaptive thresholding method, VCA method)。傳統(tǒng)閾值分割方法在分割過程中只考慮了圖像灰度特性與空間信息,而忽略了視覺對于分割結果的影響。與傳統(tǒng)閾值分割方法不同,我們的方法將閾值選擇過程與人類視覺特性相融合,提出一種視覺一致的自適應圖像閡值分割方法。首先根據像素的灰度信息構建兩幅子圖；然后根據人類視覺特性定義目標函數,定量刻畫圖像中的視覺信息；通過對目標函數優(yōu)化求解,得到每幅子圖的全局最優(yōu)閾值；最后再利用圖像的局部特性,進行局部自適應閾值操作得到最終的閡值分割結果。由于在閾值分割的過程中,我們利用人類視覺特性對前景與背景進行自動分離,使得分割后的二值圖像獲得了較好的視覺效果,其整體的視覺質量更符合人類視覺感知。(2)提出一種新的選取網格模型關鍵點的方法,我們稱之為種子點,并在此基礎上提出一種有意義的網格分割方法。首先找到網格模型中的尖銳特征區(qū)域,選出每個區(qū)域中最為顯著的網格頂點構建候選點集合；用于分割的種子點是特征點集合的子集,通過最大化頂點集合之間的差異度對特征點集合進行篩選,從而得到網格模型的種子點集合；在此基礎上,利用種子點集合對網格模型進行分割；根據視覺理論中的最小準則可知,人們通常將模型中的凹區(qū)域看成潛在的分割邊界,為此我們利用網格模型的幾何屬性定義網格頂點間的距離函數,該函數由弧長,角距離和修正項三部分組成；最后通過對網格頂點進行聚類,得到視覺上有意義的分割結果。(3)提出一種基于視覺顯著性與譜聚類的網格分割方法。我們將三維模型在原空間中的分割問題轉化為譜空間的聚類問題。通過將視覺顯著性與譜聚類過程相結合,生成有視覺意義的網格分割結果。首先根據視覺理論中的最小值規(guī)則制定多個判斷準則以確定網格凹區(qū)域；然后根據網格的顯著性來刻畫頂點間的關聯(lián)度,從而定義出網格模型的Laplacian矩陣；通過計算矩陣的特征向量,我們可以對原網格模型進行k維譜空間嵌入,從而將模型在原空間域中的分割問題轉化為譜空間的聚類問題：最后通過分析網格的顯著性確定每一類的初始聚類中心,并利用高斯混合模型(Gaussian Mixture Model, GMM)聚類方法對嵌入空間的網格頂點進行聚類,最終得到有視覺意義的網格分割結果。實驗結果表明該算法可以得到視覺上有意義的分割結果,特別是對于凹凸特征明顯,以及具有核心部件和分支結構的模型,該方法可以產生較好的視覺結果。
[Abstract]:Because of the progress and perfection of computer science and related mathematics theory, graphic image processing has become one of the most active research directions. The image segmentation problem has always been an important research topic in this field. After years of research and development, graphic image segmentation technology has been widely used in computer animation, Medical image processing, virtual reality, computing visualization and many other fields. In many image processing work, some areas of the image need to be extracted. We can divide the pixels by image segmentation technology and separate the target area from the background. This makes the image segmentation a key step in the image recognition and understanding from the underlying image processing. Human can accurately separate the target area from the image, but it is not an easy thing for the computer. In most cases, the quality of the image itself, and the complexity and diversity of the image content are in most cases. The influence of other factors makes it difficult for the computer to divide the image according to human understanding. At present, the image segmentation method is mostly based on the similarity of each region in the image, or the difference of feature as the criterion, and divides the image into several regions which are not intersected with each other, but rarely applies the human visual consciousness to the process of segmentation. The segmentation results are usually far from the human visual perception. Therefore, how to combine the visual characteristics with the image segmentation technology to produce the segmentation results more consistent with the human visual perception is still a subject worth studying in the image processing and related fields. With the progress of data acquisition and the continuous development of modeling technology, three Graphic data has become a new form of digital media representation, and the analysis and processing of 3D model has become a hot topic in the field of computer graphics. Like image segmentation, people still want to use human visual characteristics and related theories to make "meaningful" segmentation of the grid model, and get many of them. A component with visual meaning or physical meaning to facilitate understanding of the model at a higher level. However, in general, the definition of a "meaningful" component is very subjective, and the definition of "meaningful" segmentation is also different in different application backgrounds. In addition, compared with the two-dimensional image data, three In addition to geometric properties, dimensional models also contain complex spatial and topological information, which makes the segmentation of 3D models more challenging. Therefore, how to make use of human visual characteristics to produce mesh segmentation results more consistent with visual perception is still a subject worthy of further study. The segmentation results are one of the issues of common concern for image segmentation and 3D model segmentation. From the perspective of human visual characteristics, this paper studies the problem of image segmentation and mesh segmentation, and proposes new image segmentation algorithms and mesh segmentation algorithms. The main research work includes the following points: (1) a conformation is proposed. The image adaptive threshold segmentation method (Visual consistent adaptive thresholding method, VCA method) for human visual characteristics. The traditional threshold segmentation method only takes into account the image grayscale characteristics and spatial information in the segmentation process, but neglects the effect of vision on the segmentation results. Our method is different from the traditional threshold segmentation method. Combining the threshold selection process with the human visual characteristics, a vision consistent adaptive image segmentation method is proposed. First, two subgraphs are constructed according to the pixel gray information, then the target function is defined according to the human visual characteristics, and the visual information in the image is quantitatively depicted. The global optimal threshold of the amplitude subgraph; finally, using the local characteristics of the image, the final threshold segmentation results are obtained by local adaptive threshold operation. In the process of threshold segmentation, we use the human visual characteristics to automatically separate the foreground and the background, and make the two value images after the score cut better visual effect, The visual quality of the whole is more in line with human visual perception. (2) a new method of selecting key points of the grid model is proposed. We call it the seed point. On this basis, we propose a meaningful mesh segmentation method. First, we find the sharp feature area in the grid model, and select the most significant grid vertex in each area. The seed point set for the segmentation is a subset of the set of feature points. The seed point set of the grid model is obtained by selecting the set of the feature points by the difference degree of the maximum vertex sets. On this basis, the mesh model is segmented by the seed set. According to the minimum criterion in the visual theory, It is known that the concave region in the model is usually considered as a potential segmentation boundary, so we use the geometric attributes of the grid model to define the distance function between the vertices of the grid. This function is composed of three parts: arc length, angular distance and correction term. Finally, by clustering the vertices of the grid, the visual meaningful segmentation results are obtained. (3) proposed A mesh segmentation method based on visual significance and spectral clustering. We transform the segmentation problem in the original space into the clustering problem in the spectral space. By combining the visual significance with the spectral clustering process, the results of the mesh segmentation with visual meaning are generated. The criterion is determined to determine the concave area of the grid, and then the correlation degree between the vertices is depicted according to the significance of the grid, and the Laplacian matrix of the mesh model is defined. By calculating the eigenvectors of the matrix, we can embed the K dimensional space of the original mesh model to transform the segmentation problem into the spectrum in the original space domain. Clustering problem of space: finally, the initial clustering center of each class is determined by analyzing the saliency of the grid, and the Gauss hybrid model (Gaussian Mixture Model, GMM) clustering method is used to cluster the mesh vertices of the embedded space. Finally, the results of the mesh segmentation with visual sense are obtained. The experimental results show that the algorithm can be viewed. The results of meaningful segmentation, especially the obvious characteristics of concave and convex, and the model with core components and branch structures, can produce better visual results.
【學位授予單位】：吉林大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41

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