本文選題：變分水平集 + 圖像分割��； 參考：《中北大學(xué)》2017年碩士論文

【摘要】：圖像分割是一項(xiàng)應(yīng)用廣泛的圖像處理技術(shù),可很大程度的減少后面高級(jí)圖像處理所需的數(shù)據(jù)量,且不影響結(jié)構(gòu)特征相關(guān)的信息,在圖像處理中起關(guān)鍵作用。在圖像分割中出現(xiàn)誤差將影響后續(xù)處理圖像的有效性,所以近半個(gè)世紀(jì)以來,學(xué)者們不斷提出各種分割方法來提高分割的精度和準(zhǔn)確性,在改進(jìn)分割的方法上做出不少工作。而變分水平集應(yīng)用在圖像分割中也有顯著的效果,廣泛應(yīng)用在醫(yī)學(xué),交通,工業(yè),農(nóng)業(yè)等各種領(lǐng)域。Samson等學(xué)者提出將FCM聚類與變分水平集方法相結(jié)合進(jìn)行圖像分割,該方法具有很好的圖像分割效果。但這種模型需周期性不停地重新初始化,從而影響圖像分割時(shí)間。本文通過引入內(nèi)部能量函數(shù)的H1正則化,使得水平集函數(shù)在演化過程中無需重新初始化從而節(jié)約時(shí)間。用文中的方法與傳統(tǒng)的Samson模型和FCM算法分割圖像對(duì)比實(shí)驗(yàn),結(jié)果表明,本文方法具有更短的運(yùn)行時(shí)間和更好的分割效果。其次,針對(duì)傳統(tǒng)的CV模型在圖像分割中不能很好的分割灰度不均的圖像,分析了Lee-Seo和Li-Kim兩種改進(jìn)模型在圖像分割方面的性能,提出一種新的能量函數(shù),并給出了一種基于改進(jìn)的能量函數(shù)的圖像分割算法。三組實(shí)驗(yàn)結(jié)果表明,與CV模型、Lee-Seo模型、Li-Kim模型比較起來,改進(jìn)后的算法具有運(yùn)行時(shí)間短,迭代次數(shù)少,分割效果好等優(yōu)點(diǎn)。最后,對(duì)本文做出總結(jié),提出不足與今后可以繼續(xù)學(xué)習(xí)研究的方向。
[Abstract]:Image segmentation is a widely used image processing technology, which can greatly reduce the amount of data needed for the subsequent high-level image processing, and does not affect the information related to structural features. It plays a key role in image processing. The error in image segmentation will affect the effectiveness of the subsequent image processing, so in the last half century, scholars have proposed a variety of segmentation methods to improve the accuracy and accuracy of segmentation, and a lot of work has been done to improve the segmentation method. And the application of variational level set in image segmentation also has remarkable effect. It is widely used in medicine, traffic, industry, agriculture and other fields. Samson and other scholars put forward the combination of FCM clustering and variational level set method for image segmentation. This method has good image segmentation effect. However, this model needs to be reinitialized periodically, which affects the time of image segmentation. By introducing the H _ 1 regularization of the internal energy function, the level set function does not need to be reinitialized in the evolution process, thus saving time. Compared with the traditional Samson model and FCM algorithm, the experimental results show that the proposed method has shorter running time and better segmentation effect. Secondly, aiming at the fact that the traditional CV model can not segment the image with uneven grayscale, the performance of the two improved models, Lee-Seo and Li-Kim, in image segmentation is analyzed, and a new energy function is proposed. An image segmentation algorithm based on improved energy function is presented. The results of three groups of experiments show that compared with the CV model Lee-Seo model and Li-Kim model, the improved algorithm has the advantages of shorter running time, fewer iterations and better segmentation effect. Finally, this paper makes a summary, proposes the insufficiency and may continue to study the research direction in the future.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

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2 謝振平;王士同;;融合模糊聚類的Mumford-Shah模型[J];電子學(xué)報(bào);2008年01期

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本文編號(hào)：1936769