視覺顯著性檢測模型研究及應(yīng)用
發(fā)布時間：2018-08-02 10:46
【摘要】：人類視覺系統(tǒng)在面對復(fù)雜自然場景時,具有快速搜索感興趣目標的能力,這種能力我們稱之為視覺注意。在人類生存與發(fā)展的過程中,視覺注意扮演著至關(guān)重要的角色。視覺注意和人類如何感知、處理視覺刺激緊密相關(guān),并且正在被包括認知心理學(xué)、神經(jīng)生物學(xué)和計算機視覺在內(nèi)的多個學(xué)科進行研究。隨著認知心理學(xué)和神經(jīng)生物學(xué)的不斷發(fā)展,通過對視覺機理的研究發(fā)現(xiàn),人類視覺對場景中目標的選擇性可分為兩個階段：一個快速的、無意識的、數(shù)據(jù)驅(qū)動的、自底向上的階段和一個較慢的、有意識的、任務(wù)驅(qū)動的、自頂向下的階段.而與視覺注意緊密相連的概念就是視覺顯著性,他是指導(dǎo)視覺注意的一個關(guān)鍵注意機制。圖像顯著性區(qū)域檢測研究的目的是快速定位顯著性區(qū)域并反映顯著性區(qū)域的顯著程度。視覺顯著性區(qū)域檢測在圖像處理中有著廣泛的應(yīng)用,包括圖像分割、目標識別、自適應(yīng)壓縮、內(nèi)容敏感圖像編輯、圖像檢索、目標檢測、目標跟蹤、圖像質(zhì)量評價等。本文從視覺注意機制的研究出發(fā),對視覺顯著性檢測與應(yīng)用中的一些關(guān)鍵問題進行了較為深入的研究,提出了一些新的思想和算法。論文的主要工作與貢獻包括：(1)針對已有局部對比度和全局對比度建模方法存在的不足,本文提出了一種基于條件隨機場融合全局特征的顯著性區(qū)域檢測方法。該方法首先采用唯一性、顏色空間分布等全局特征計算相應(yīng)的顯著圖：其次在條件隨機場框架下融合多個顯著圖,通過顯著性區(qū)域與背景區(qū)域的區(qū)域標注實現(xiàn)顯著性區(qū)域初步檢測；然后采用基于顯著性區(qū)域的高斯模型計算目標先驗圖,并對全局特征顯著圖進行高斯濾波；最后再利用條件隨機場融合濾波之后的顯著圖來實現(xiàn)更加精確的顯著性檢測。實驗結(jié)果表明該方法能均勻致密的凸顯顯著性區(qū)域,有效的抑制背景干擾,并具有較高的檢測準確率與召回率。(2)基于視覺機制挖掘可應(yīng)用的更高層次的顯著性先驗特征,本文提出了一種融合多級顯著性特征的顯著性目標檢測方法。該方法融合了基于像素級的局部對比度、基于區(qū)域級的全局對比度以及基于目標級的背景先驗信息。該方法基于凸包檢測技術(shù)使用底層的視覺線索從背景分離顯著性目標�；诔跫壍臋z測結(jié)果提取背景模版,利用PCA計算背景先驗信息。為了抑制背景干擾,該方法采用目標中心先驗信息精煉局部對比度特征和全局對比度特征。在公開的數(shù)據(jù)集上的實驗表明,該方法所得到的顯著圖能較好的凸顯顯著性目標.同時也證明Otsu自適應(yīng)閾值方法可以用來產(chǎn)生高質(zhì)量的目標分割結(jié)果。(3)針對視覺顯著性在目標跟蹤過程中的應(yīng)用研究,本文提出了一種基于視覺注意的目標跟蹤算法。該算法首先采用基于背景先驗的視覺顯著性檢測算法來提取目標的顯著性特征,其次采用基于貝葉斯決策理論的前景背景分類方法來提取目標的運動特征,然后利用顯著特征引導(dǎo)運動特征與顏色特征進行目標狀態(tài)估計,最后結(jié)合自適應(yīng)粒子濾波形成目標跟蹤算法。實驗結(jié)果表明在復(fù)雜場景下,該算法相對于現(xiàn)有的目標跟蹤算法具有較強的魯棒性,對光照變化、姿態(tài)變化、目標遮擋、快速運動、復(fù)雜背景等具有較好的跟蹤效果。(4)針對槍球聯(lián)動接力跟蹤過程中的目標離開槍機畫面后在球機中初始定位問題,本文提出了一種基于視覺注意的槍球聯(lián)動接力跟蹤方法。該方法采用網(wǎng)格結(jié)合插值算法實現(xiàn)在球機中的目標放大跟蹤,當目標離開槍機畫面時,利用視覺顯著性檢測算法計算候選區(qū)域,利用槍機保存的目標模版在候選區(qū)域中搜索匹配區(qū)域,確定目標在球機場景中的位置,最后利用Mean Shift跟蹤算法實現(xiàn)球機的主動跟蹤。實驗結(jié)果表明本文提出的基于視覺注意的槍球聯(lián)動接力跟蹤具有較好的實時跟蹤效果。
[Abstract]:In the face of complex natural scenes, the human visual system has the ability to quickly search for a target of interest, which we call visual attention. Visual attention plays a vital role in the process of human survival and development. Visual attention and human perception are closely related to visual stimuli, and are being included. The study of cognitive psychology, neurobiology and computer vision. With the continuous development of cognitive psychology and neurobiology, the study of visual mechanisms found that human vision can be divided into two stages: a fast, unconscious, data driven, bottom-up. The stage and a slow, conscious, task driven, top-down stage. The concept of close connection with visual attention is visual significance. He is a key attention mechanism to guide visual attention. The purpose of the image saliency region detection study is to quickly locate the significant region and reflect the significance of the significant region. Degree. Visual saliency region detection has a wide range of applications in image processing, including image segmentation, target recognition, adaptive compression, content sensitive image editing, image retrieval, target detection, target tracking, image quality evaluation and so on. This paper, starting from the research of visual attention mechanism, discusses some key points in visual significance detection and application. Some new ideas and algorithms are proposed. The main work and contributions of this paper are as follows: (1) in view of the shortcomings of the existing local contrast and the global contrast modeling method, a significant regional detection method based on the global characteristics of conditional random fields is proposed. We use the uniqueness, the color space distribution and other global characteristics to calculate the corresponding significant graphs. Secondly, multiple significant graphs are fused under the conditional random field framework, and the significant region detection is realized through the regional annotation of the significant region and the background region. Then the Gauss model based on the saliency region is used to calculate the prior map of the target, and the whole area is calculated. The characteristic salient image of the bureau is used to carry out Gauss filtering; finally, a more accurate detection is achieved by using the salient graph following the fusion filter of the airport. The experimental results show that the method can highlight the significant region, effectively suppress the background interference, and have high detection accuracy and recall. (2) the visual machine is based on the visual machine. This method combines the local contrast based on the pixel level, the global contrast based on the regional level and the backview prior information based on the target level. This method is based on the convex packet detection technique. The underlying visual cues are used to separate the significant targets from the background. The background template is extracted based on the primary detection results and the background information is calculated using the PCA. In order to suppress the background interference, the method uses the target center prior information to refine the local contrast characteristics and the global contrast characteristics. The experiment on the open data set shows that this method is used to extract the background information. The remarkable graph obtained by the method can better highlight the significant target. It also proves that the Otsu adaptive threshold method can be used to produce high quality target segmentation results. (3) aiming at the application of visual significance in target tracking, a target tracking algorithm based on visual attention is proposed in this paper. The visual saliency detection algorithm of background prior is used to extract the significant feature of the target. Secondly, the foreground background classification method based on Bayesian decision theory is used to extract the motion features of the target, and then the target state is estimated by using the significant feature to guide the motion feature and color feature. Finally, the adaptive particle filter is combined to form the target state. The experimental results show that in the complex scene, the algorithm has strong robustness against the existing target tracking algorithm, and has good tracking effect on illumination change, attitude change, target occlusion, fast motion, complex background and so on. (4) in the course of the gun ball linkage relay tracking, the target leaves the gun frame. In this paper, the problem of initial positioning in the ball machine is presented. This paper proposes a method of tracking the joint force of the gun ball based on visual attention. This method uses the mesh and interpolation algorithm to achieve the target amplification and tracking in the ball machine. When the target leaves the gun, the candidate region is calculated by the visual significance detection algorithm, and the target template saved by the gun is used. In the candidate region, the matching area is searched, the location of the target in the ball scene is determined, and the Mean Shift tracking algorithm is used to achieve the active tracking of the ball machine. The experimental results show that the tracking of the gun ball joint relay based on visual attention has good real-time tracking effect.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
，
本文編號：2159180
[Abstract]:In the face of complex natural scenes, the human visual system has the ability to quickly search for a target of interest, which we call visual attention. Visual attention plays a vital role in the process of human survival and development. Visual attention and human perception are closely related to visual stimuli, and are being included. The study of cognitive psychology, neurobiology and computer vision. With the continuous development of cognitive psychology and neurobiology, the study of visual mechanisms found that human vision can be divided into two stages: a fast, unconscious, data driven, bottom-up. The stage and a slow, conscious, task driven, top-down stage. The concept of close connection with visual attention is visual significance. He is a key attention mechanism to guide visual attention. The purpose of the image saliency region detection study is to quickly locate the significant region and reflect the significance of the significant region. Degree. Visual saliency region detection has a wide range of applications in image processing, including image segmentation, target recognition, adaptive compression, content sensitive image editing, image retrieval, target detection, target tracking, image quality evaluation and so on. This paper, starting from the research of visual attention mechanism, discusses some key points in visual significance detection and application. Some new ideas and algorithms are proposed. The main work and contributions of this paper are as follows: (1) in view of the shortcomings of the existing local contrast and the global contrast modeling method, a significant regional detection method based on the global characteristics of conditional random fields is proposed. We use the uniqueness, the color space distribution and other global characteristics to calculate the corresponding significant graphs. Secondly, multiple significant graphs are fused under the conditional random field framework, and the significant region detection is realized through the regional annotation of the significant region and the background region. Then the Gauss model based on the saliency region is used to calculate the prior map of the target, and the whole area is calculated. The characteristic salient image of the bureau is used to carry out Gauss filtering; finally, a more accurate detection is achieved by using the salient graph following the fusion filter of the airport. The experimental results show that the method can highlight the significant region, effectively suppress the background interference, and have high detection accuracy and recall. (2) the visual machine is based on the visual machine. This method combines the local contrast based on the pixel level, the global contrast based on the regional level and the backview prior information based on the target level. This method is based on the convex packet detection technique. The underlying visual cues are used to separate the significant targets from the background. The background template is extracted based on the primary detection results and the background information is calculated using the PCA. In order to suppress the background interference, the method uses the target center prior information to refine the local contrast characteristics and the global contrast characteristics. The experiment on the open data set shows that this method is used to extract the background information. The remarkable graph obtained by the method can better highlight the significant target. It also proves that the Otsu adaptive threshold method can be used to produce high quality target segmentation results. (3) aiming at the application of visual significance in target tracking, a target tracking algorithm based on visual attention is proposed in this paper. The visual saliency detection algorithm of background prior is used to extract the significant feature of the target. Secondly, the foreground background classification method based on Bayesian decision theory is used to extract the motion features of the target, and then the target state is estimated by using the significant feature to guide the motion feature and color feature. Finally, the adaptive particle filter is combined to form the target state. The experimental results show that in the complex scene, the algorithm has strong robustness against the existing target tracking algorithm, and has good tracking effect on illumination change, attitude change, target occlusion, fast motion, complex background and so on. (4) in the course of the gun ball linkage relay tracking, the target leaves the gun frame. In this paper, the problem of initial positioning in the ball machine is presented. This paper proposes a method of tracking the joint force of the gun ball based on visual attention. This method uses the mesh and interpolation algorithm to achieve the target amplification and tracking in the ball machine. When the target leaves the gun, the candidate region is calculated by the visual significance detection algorithm, and the target template saved by the gun is used. In the candidate region, the matching area is searched, the location of the target in the ball scene is determined, and the Mean Shift tracking algorithm is used to achieve the active tracking of the ball machine. The experimental results show that the tracking of the gun ball joint relay based on visual attention has good real-time tracking effect.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
，
本文編號：2159180
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