發(fā)布時間：2018-03-16 13:00

  本文選題：小波變換　切入點：頻域調(diào)諧　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科學(xué)技術(shù)的發(fā)展,手機、電腦以及其他的一些電子設(shè)備與人們的生活產(chǎn)生了越來越緊密的聯(lián)系。人們?nèi)粘Ｉ�活中接觸的信息也呈現(xiàn)指數(shù)式增長,其中視覺信息占據(jù)了絕大部分。人的視覺系統(tǒng)如何處理這些復(fù)雜多變的信息成為一個研究熱點,與之相應(yīng)的計算機視覺概念也相繼提出,視覺注意機制與圖像顯著目標(biāo)檢測是其重要研究方向之一。在圖像處理過程中,特征提取是十分重要的環(huán)節(jié)之一。因此,本文首先介紹了圖像顯著目標(biāo)檢測領(lǐng)域常用特征和提取算法。此外,對于不同的顯著目標(biāo)檢測模型,選取不同的顏色空間會對最終的檢測效果產(chǎn)生重要的影響,所以,本文對不同的顏色空間模型進行研究為后期模型的建立做鋪墊。本文建立的第一個模型是基于小波金字塔的顯著目標(biāo)檢測模型。該模型是受Itti生物視覺注意模型的啟發(fā),采用小波金字塔模型取代高斯金字塔模型,充分發(fā)揮圖像小波變換的局部信息表達(dá)能力及多尺度空間分析能力,能夠使最終的顯著目標(biāo)圖像具有較好的輪廓信息。另外,我們將改進型的中心先驗知識融入到小波金字塔模型中,進一步增強了顯著目標(biāo)檢測效果,有利于目標(biāo)分割。文章建立的第二個模型是基于圖像局部分析與全局分析的顯著目標(biāo)檢測模型。因圖像的小波變換其具有上述優(yōu)良的性能,能夠?qū)μ崛〉奶卣鲌D像進行局部分析,進而獲得局部分析的特征顯著圖像。但是,由于其局部細(xì)節(jié)表達(dá)能力容易導(dǎo)致檢測顯著目標(biāo)不完整以及具有復(fù)雜紋理背景信息融入,因此文章進一步融入譜殘差算法對圖像做出全局分析,獲得全局分析的顯著圖像。將兩種顯著特征圖像采用非線性融合算法進行處理,得到了最終的顯著圖像。從最終的實驗結(jié)果分析來看,無論是直觀上的顯著圖像還是客觀上的P-R曲線,該模型的檢測效果要優(yōu)于其他幾種算法。本文的最后一種算法是基于圖像頻域分析顯著目標(biāo)檢測算法。事實上,無論是基于圖像小波變換的顯著目標(biāo)檢測模型還是基于譜殘算法的顯著目標(biāo)檢測方法,都是從圖像頻域角度進行分析得到的目標(biāo)檢測模型,文章進一步將頻域調(diào)諧算法與上面的基于局部和全局分析的顯著目標(biāo)檢測模型進行融合,進而獲得了一種新的基于圖像頻域分析的圖像顯著目標(biāo)檢測模型。該模型在MSRA 10K以及ECSSD兩個不同的數(shù)據(jù)庫進行檢測效果測試,結(jié)果顯示,本文的模型能夠較好的適應(yīng)不同類型的圖像,并且在圖像擁有多目標(biāo),大目標(biāo)以及復(fù)雜的背景的情況,檢測效果都要優(yōu)于其他幾種算法。
[Abstract]:With the development of science and technology, mobile phones, computers and other electronic devices have become more and more closely related to people's lives. Among them, visual information accounts for the vast majority. How human visual systems deal with these complex and changeable information has become a research hotspot, and the corresponding concepts of computer vision have been put forward one after another. Visual attention mechanism and image salient target detection are one of the important research directions. In the process of image processing, feature extraction is one of the most important links. This paper first introduces the common features and extraction algorithms in the field of image salient target detection. In addition, for different salient target detection models, the selection of different color spaces will have an important impact on the final detection effect, so, In this paper, different color space models are studied to pave the way for the establishment of later models. The first model established in this paper is a significant target detection model based on wavelet pyramid. The model is inspired by the Itti biological visual attention model. The wavelet pyramid model is used to replace Gao Si's pyramid model to give full play to the ability of local information expression and multi-scale spatial analysis of image wavelet transform, which can make the final prominent target image have better contour information. We incorporate the improved central priori knowledge into the wavelet pyramid model to further enhance the significant target detection effect. The second model, which is based on local and global analysis, is a significant target detection model, because the wavelet transform of the image has the above excellent performance. The extracted feature image can be analyzed locally, and then the feature salient image can be obtained. However, because of its ability to express local details, it is easy to detect the incomplete salient target and has complex texture background information. Therefore, the paper further integrates the spectral residual algorithm to make the global analysis of the image and obtains the salient image of the global analysis. The nonlinear fusion algorithm is used to process the two salient feature images. The final salient image is obtained. From the final analysis of the experimental results, it can be seen that both the visual salient image and the objective P-R curve, The detection effect of this model is better than that of other algorithms. The last algorithm of this paper is based on image frequency domain analysis of salient target detection algorithm. In fact, Whether the significant target detection model based on image wavelet transform or the significant target detection method based on spectral disability algorithm is the target detection model obtained from the analysis of image frequency domain. Furthermore, the frequency domain tuning algorithm is fused with the above significant target detection model based on local and global analysis. Then a new image salient target detection model based on image frequency domain analysis is obtained, which is tested in two different databases, MSRA 10K and ECSSD, and the results show that, The model in this paper can adapt to different types of images, and the detection effect is better than other algorithms when the image has multiple targets, large targets and complex background.
【學(xué)位授予單位】：山東大學(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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本文編號：1620020