本文選題：圖像配準(zhǔn) + 互信息　； 參考：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：近年來，對于圖像配準(zhǔn)的研究受到了廣泛的關(guān)注。圖像配準(zhǔn)能夠?qū)崿F(xiàn)不同拍攝條件下獲得的圖像間的匹配，已被應(yīng)用于諸多領(lǐng)域，如醫(yī)學(xué)圖像分析中的疾病狀態(tài)監(jiān)測、計算機視覺中的三維圖像重建，遙感圖像中的變換檢測。這些應(yīng)用對圖像配準(zhǔn)提出了很高的要求，，如亞像素的精確度、全自動化和實時性。目前還沒有一種完美的配準(zhǔn)方法能夠滿足所有的要求，特別是在遙感圖像處理領(lǐng)域。遙感圖像受傳感器機理、光照變換等因素的影響，圖像間灰度差異較大，增加了圖像配準(zhǔn)的難度。針對遙感圖像的特點，本文基于尺度不變特征（SIFT）和互信息（MI）提出了一種新圖像配準(zhǔn)策略。具體內(nèi)容闡述如下： (1)提出一種基于尺度不變性的SIFT特征錯誤匹配濾除方法。該方法計算待配準(zhǔn)圖像中兩兩特征間距離與參考圖像中相應(yīng)特征間距離的比值，形成尺度直方圖，直方圖的波峰對應(yīng)兩幅圖像的尺度比，直方圖中邊緣點被認(rèn)為是錯誤配準(zhǔn)并濾除。尺度直方圖利用了SIFT的尺度不變性，獲得更加準(zhǔn)確的濾除結(jié)果。 (2)提出一種新的最大化互信息初始解選擇策略。該策略獲得SIFT匹配，并濾除其中的錯誤匹配，然后利用最小二乘法估計出配準(zhǔn)參數(shù)。通過該策略獲得的配準(zhǔn)參數(shù)在最優(yōu)解的附近，因此將其作為最大化互信息的初始解能夠?qū)崿F(xiàn)最大化互信息全自動化，降低互信息迭代次數(shù)，加快搜索算法收斂。 (3)利用Levenberg Marquardt最大化互信息實現(xiàn)遙感圖像配準(zhǔn)。Levenberg Marquardt算法結(jié)合了梯度下降法的魯棒性和牛頓法的快速收斂性。利用該算法的優(yōu)越性能，能夠快速的獲得精確配準(zhǔn)參數(shù)。
[Abstract]:In recent years, the research on image registration has received extensive attention. Image registration has been applied to many fields, such as disease state monitoring in medical image analysis, 3D image reconstruction in computer vision and transform detection in remote sensing image. These applications require very high image registration, such as subpixel accuracy, full automation and real-time. At present, there is no perfect registration method to meet all the requirements, especially in the field of remote sensing image processing. Remote sensing images are affected by sensor mechanism, illumination transformation and other factors. The difference of gray level between images increases the difficulty of image registration. According to the characteristics of remote sensing images, a new image registration strategy based on scale-invariant features (sift) and mutual information (MII) is proposed in this paper. The details are as follows: A scale invariant based SIFT feature matching filtering method is proposed. This method calculates the ratio of the distance between the pairwise features in the image to be registered and the distance between the corresponding features in the reference image, and forms the scale histogram, and the wave peak of the histogram corresponds to the scale ratio of the two images. Edge points in a histogram are considered to be mismatched and filtered. The scale histogram utilizes the scale invariance of SIFT to obtain more accurate filtering results. A new strategy of maximizing mutual information initial solution selection is proposed. The SIFT matching is obtained, the error matching is filtered out, and the registration parameters are estimated by the least square method. The registration parameters obtained by this strategy are near the optimal solution. Therefore, as the initial solution of maximizing mutual information, the algorithm can realize the full automation of maximization of mutual information, reduce the number of iterations of mutual information, and accelerate the convergence of the search algorithm. 3) using Levenberg / Marquardt to maximize mutual information to realize remote sensing image registration. Levenberg Marquardt algorithm combines the robustness of gradient descent method and the fast convergence of Newton method. By using the superior performance of the algorithm, accurate registration parameters can be obtained quickly.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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