本文選題：圖像分割　切入點：灰度不均勻　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計算機技術(shù)的日益發(fā)展,數(shù)字圖像處理在信息社會中扮演著不可或缺的角色,成為一股推動社會進步的動力源。在數(shù)字圖像處理中,圖像分割作為關(guān)鍵而基礎(chǔ)的一環(huán),已在實際生產(chǎn)生活中起到越來越大的作用。圖像分割的目的是將一幅圖像按照不同的特征分割成具有均勻特性的子區(qū)域,并將感興趣的目標(biāo)從背景區(qū)域分離出來。目前,學(xué)者們已經(jīng)提出了許多行之有效的圖像分割算法。然而,由于存在灰度不均勻、噪聲、背景結(jié)構(gòu)復(fù)雜等問題,圖像分割依舊是一項具有挑戰(zhàn)性的任務(wù)�；谒郊幕顒虞喞Ｐ陀捎谀芴幚硗負浣Y(jié)構(gòu)變化,數(shù)值實現(xiàn)簡單,能完成目標(biāo)整體分割等特點,近年來成為圖像分割領(lǐng)域的研究熱點。本文針對圖像分割中灰度不均勻、噪聲和初始輪廓敏感問題,在LGDF(Local Gaussian Distribution Fitting)模型的基礎(chǔ)上進行改進,取得了如下研究成果:(1)針對灰度不均勻和初始輪廓敏感的問題,提出一種基于多尺度局部特征的活動輪廓模型(MS_LGDF)。首先,非均勻光照引起的灰度不均勻在圓形散射狀中平滑緩慢變化,該模型采用圓形區(qū)域來獲取更多的局部信息;其次,針對局部區(qū)域灰度的變化程度不同,提出利用多尺度結(jié)構(gòu)與均值濾波器相結(jié)合的方法獲得多尺度局部灰度信息;最后,通過轉(zhuǎn)換灰度不均勻模型得到一個逼近真實信息的圖像,并將其融合進LGDF模型,構(gòu)造出基于多尺度局部特征的能量泛函。與傳統(tǒng)的LGDF模型相比,由于多尺度結(jié)構(gòu)弱化了灰度不均勻的影響,本文提出的MS_LGDF改進模型分割速度快、精度高,對初始輪廓具有較強的魯棒性。(2)針對灰度不均勻和噪聲問題,提出一種基于于局部魯棒統(tǒng)計的活動輪廓模型(LRS_LGDF)。首先,利用局部區(qū)域中像素的四分位距(IQR)、平均絕對偏差(MAD)和中中位數(shù)(MED)構(gòu)造局部魯棒統(tǒng)計信息,其中,IQR和MAD的作用是銳化目標(biāo)邊界,從而加快輪廓曲線的演化速度,MED則是用于減弱圖像噪聲;其次,結(jié)合LGDF與局部魯棒統(tǒng)計信息構(gòu)成新的數(shù)據(jù)擬合項;最后,將其與另外兩個內(nèi)部約束項一起融入變分水平集方法,構(gòu)造出LRS_LGDF活動輪廓模型。與傳統(tǒng)的LGDF模型相比,由于局部魯棒統(tǒng)計信息的引入,本文提出的LRS_LGDF改進模型分割速度快、精度高,對噪聲具有較強的魯棒性。
[Abstract]:With the development of computer technology, digital image processing plays an indispensable role in the information society and becomes a power source to promote social progress. The purpose of image segmentation is to divide an image into subregions with uniform characteristics according to different features, and to separate the objects of interest from the background area. Many effective image segmentation algorithms have been proposed by scholars. However, there are many problems such as uneven gray scale, noise, complex background structure, etc. Image segmentation is still a challenging task. The active contour model based on the level set can deal with the topological structure change, the numerical realization is simple, and the target can be segmented as a whole. In recent years, it has become a hot topic in the field of image segmentation. In this paper, we improve the LGDF(Local Gaussian Distribution Fitting-based model for the problems of grayscale inhomogeneity, noise and initial contour sensitivity in image segmentation. A new active contour model based on multi-scale local features is proposed to solve the problem of inhomogeneity of gray scale and sensitivity of initial contour. First of all, a new active contour model based on multi-scale local features is proposed. The gray inhomogeneity caused by non-uniform illumination changes slowly and smoothly in the circular scattering form. The model uses circular region to obtain more local information. Secondly, according to the varying degree of gray level in local region, the degree of variation of gray level in local area is different. The method of combining multi-scale structure with mean value filter is proposed to obtain multi-scale local gray level information. Finally, an image approximating real information is obtained by converting gray scale non-uniform model into LGDF model. The energy functional based on multi-scale local features is constructed. Compared with the traditional LGDF model, the improved MS_LGDF model proposed in this paper has a fast segmentation speed and a high accuracy, because the multi-scale structure weakens the influence of gray inhomogeneity. A new active contour model based on local robust statistics is proposed to solve the problem of grayscale inhomogeneity and noise. Firstly, a new active contour model based on local robust statistics is proposed. The local robust statistical information is constructed by using the quartile distance of the pixels in the local region, the mean absolute deviation (MAD) and the median (MED), in which the role of IQR and MAD is to sharpen the target boundary. In order to speed up the evolution of contour curve, it is used to attenuate image noise. Secondly, combining LGDF with local robust statistical information, a new data fitting term is constructed. Finally, it is combined with the other two internal constraint terms into the variational level set method. The LRS_LGDF active contour model is constructed. Compared with the traditional LGDF model, the improved LRS_LGDF model proposed in this paper has the advantages of fast segmentation speed, high precision and strong robustness to noise due to the introduction of local robust statistical information.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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