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  本文關(guān)鍵詞： 視覺顯著性 顯著圖 多尺度分析 圖像分割　出處：《西南大學》2017年碩士論文　論文類型：學位論文

【摘要】：視覺是人類認知世界獲取信息的主要途徑,使人能夠感知復雜、變化的環(huán)境。因為人眼攝入圖像的整體性和人類視覺神經(jīng)系統(tǒng)處理信息的高度并行性,人類辨識圖像并判斷出其感興趣區(qū)域是非常容易的事。隨著計算機、通信和數(shù)字媒體為代表的信息技術(shù)迅速發(fā)展,視覺顯著性檢測已廣泛應用于的遙感圖像、醫(yī)學圖像處理、機器人視覺控制等領(lǐng)域。對視覺顯著性檢測技術(shù)進行研究與應用,使計算機具有人類視覺系統(tǒng)相似的信息處理能力,高效且迅速地進行圖像處理,對提升圖像理解系統(tǒng)與圖像處理系統(tǒng)的性能,提高圖像處理技術(shù)的實際應用水平都有非常重要的作用。針對現(xiàn)有顯著性檢測方法提取圖像顯著性目標區(qū)域的準確率以及效率較低的問題,基于人類視覺神經(jīng)系統(tǒng)的選擇性和主動性,結(jié)合圖像底層的顏色對比特征、顏色分布特征、位置信息,融合多通道特征,進行多尺度分析,計算顯著性特征提取出圖像的顯著性區(qū)域。主要工作包括以下幾個方面:(1)針對傳統(tǒng)顯著性檢測方法沒有對圖像本身先驗信息加以利用的問題,提出融合背景模型和顏色特征的視覺顯著性檢測方法。對圖像進行slic超像素分割和顏色空間轉(zhuǎn)換,構(gòu)造圖像橢圓背景模型,在lab三個顏色通道上,分別計算橢圓內(nèi)部區(qū)域的顯著性特征和四個邊緣背景區(qū)域的奇異性特征,線性融合不同特征通道的內(nèi)部顯著圖和邊緣背景顯著圖獲得最終顯著圖。(2)針對現(xiàn)有檢測方法提取出的顯著性區(qū)域清晰程度不夠,計算效率比較低的問題,提出基于視覺中心轉(zhuǎn)移的視覺顯著性檢測方法。對圖像進行slic預分割基礎(chǔ)之上,結(jié)合圖像的顏色對比特征、顏色分布特征和位置特征,提取出圖像顯著性區(qū)域,采用視覺轉(zhuǎn)移機制模擬人眼的視覺注意中心轉(zhuǎn)移過程,對圖像進行多尺度分析,融合不同尺度顯著圖獲得最終顯著圖。我們把上述兩種方法在MSRA數(shù)據(jù)庫上進行了驗證實驗,并和現(xiàn)有的檢測方法進行對比。實驗結(jié)果驗證了我們方法的能更清晰且完整地提取出圖像顯著性目標區(qū)域。
[Abstract]:Vision is the main way for human beings to acquire information from the world, which enables people to perceive complex and changing environments, because of the integrity of human visual images and the high parallelism of human visual nervous systems in processing information. With the rapid development of information technology represented by computers, communications and digital media, visual salience detection has been widely used in remote sensing images, medical image processing, In the field of robot vision control, the visual salience detection technology is studied and applied to make the computer have the similar information processing ability of human visual system, and carry out image processing efficiently and quickly. To improve the performance of image understanding system and image processing system, It is very important to improve the practical application level of image processing technology. Based on the selectivity and initiative of human visual nervous system, combining the color contrast feature, color distribution feature, position information and multi-channel feature of the image bottom, the multi-scale analysis is carried out. The main work includes the following aspects: (1) aiming at the problem that the traditional salience detection method does not make use of the prior information of the image itself, A method of visual salience detection based on fusion of background model and color feature is proposed. The image is segmented by slic super-pixel and transformed into color space. The elliptical background model of image is constructed on the three color channels of lab. The salient features of the inner region of the ellipse and the singularity of the four edge background regions are calculated respectively. Linear fusion of internal salience map of different feature channels and edge background salience map to obtain final salience map. 2) aiming at the problem that the clear degree of significant region extracted by existing detection methods is not enough and the computational efficiency is low. A visual salience detection method based on the shift of visual center is proposed. On the basis of slic pre-segmentation, the salient region of the image is extracted by combining the color contrast feature, the color distribution feature and the location feature of the image. The visual shift mechanism is used to simulate the visual attention center transfer process of human eyes, and the image is analyzed by multi-scale analysis, and the final salience map is obtained by fusion of different scale salience maps. The two methods mentioned above are verified on the MSRA database. Compared with the existing detection methods, the experimental results show that our method can extract the significant target region more clearly and completely.
【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前2條

1 孫浪;唐雁;苗宗霞;謝松山;何熒;;基于多尺度多通道均值的視覺顯著性檢測[J];蘭州大學學報(自然科學版);2015年05期

2 王瑞;余宗鑫;杜林峰;萬旺根;;結(jié)合圖像信號顯著性的自適應分塊壓縮采樣[J];中國圖象圖形學報;2013年10期

相關(guān)博士學位論文 前1條

1 李志強;視覺顯著性模型研究及其在影像處理中的應用[D];上海交通大學;2009年

相關(guān)碩士學位論文 前2條

1 萬莉;基于仿生視覺機理的多源圖像融合[D];天津師范大學;2013年

2 李崇飛;基于視覺注意的顯著區(qū)域檢測算法研究[D];國防科學技術(shù)大學;2011年

，

本文編號：1533952