面向圖像多源屬性的協(xié)同分割方法研究
發(fā)布時(shí)間：2018-08-01 09:32
【摘要】：如何在場(chǎng)景復(fù)雜的圖片中快速得到用戶(hù)感興趣的目標(biāo),正成為如今計(jì)算機(jī)視覺(jué)和模式識(shí)別領(lǐng)域的熱點(diǎn)和難點(diǎn)。圖像分割作為一種提取目標(biāo)的有效途徑一直以來(lái)都受到學(xué)者們的廣泛關(guān)注,也已經(jīng)取得了較多的研究成果。但是依舊存在較多的難點(diǎn),比如:圖像包含的信息越來(lái)越多,圖像的特征也越來(lái)越豐富,單一的特征已經(jīng)不能滿(mǎn)足如今的技術(shù)要求;在分割粒度方面,基于像素級(jí)的分割框架往往會(huì)導(dǎo)致分割目標(biāo)的不完整性,而一些基于區(qū)域的分割框架則存在細(xì)節(jié)方面的丟失,并且較為依賴(lài)預(yù)分割區(qū)域的準(zhǔn)確性。本文針對(duì)圖像的多源屬性協(xié)同分割問(wèn)題,主要進(jìn)行了如下幾個(gè)方面的創(chuàng)新工作:首先,提出了一種有效的紋理建模方式。通過(guò)對(duì)傳統(tǒng)的多尺度結(jié)構(gòu)張量進(jìn)行精簡(jiǎn)和非線性濾波處理得到非線性精簡(jiǎn)多尺度結(jié)構(gòu)張量,并將非線性精簡(jiǎn)多尺度結(jié)構(gòu)張量與全局變分流結(jié)合構(gòu)成我們所使用的紋理描述子。采用圖割框架對(duì)該紋理描述子的有效性進(jìn)行實(shí)驗(yàn),通過(guò)與常用紋理特征的實(shí)驗(yàn)對(duì)比驗(yàn)證了該紋理描述子在紋理描述力上有較好的效果,而且其較低的維度特質(zhì)也給后續(xù)的概率建模帶來(lái)了效率上的提升。其次,提出了基于虛擬節(jié)點(diǎn)的圖像多源屬性的協(xié)同分割框架,并將該框架用于多特征的協(xié)同分割。以L*a*b顏色特征和我們所提出的紋理建模方式為例對(duì)基于虛擬節(jié)點(diǎn)的顏色紋理協(xié)同分割方法進(jìn)行了實(shí)驗(yàn)。通過(guò)與傳統(tǒng)的顏色紋理能量混合模型和單一的特征的分割結(jié)果進(jìn)行對(duì)比,說(shuō)明了該分割框架能夠較好的吸納不同特征分割中的優(yōu)勢(shì)部分。最后,提出了基于上下文信息的圖像多源屬性的協(xié)同分割框架,并將該框架用于多粒度的協(xié)同分割。以邊緣增強(qiáng)的均值漂移算法得到的同質(zhì)區(qū)域粗粒度和原像素細(xì)粒度為例對(duì)基于上下文信息的粗細(xì)粒度協(xié)同分割方法進(jìn)行了實(shí)驗(yàn)。通過(guò)與基于單一粒度的分割結(jié)果進(jìn)行對(duì)比,說(shuō)明該分割框架能夠在細(xì)節(jié)和目標(biāo)整體性上面表現(xiàn)良好。本文通過(guò)大量的仿真實(shí)驗(yàn)驗(yàn)證了本文紋理建模方式、基于虛擬節(jié)點(diǎn)的多特征協(xié)同分割和基于上下文信息的多粒度協(xié)同分割的實(shí)效性和可用性,并具有良好的應(yīng)用前景。
[Abstract]:How to quickly get users interested in the complex scene is becoming a hot and difficult point in the field of computer vision and pattern recognition. As an effective way to extract the target, image segmentation has been widely concerned by scholars and has also taken more research results. There are many difficulties, such as: more and more information is included in the image, and the features of images are becoming more and more rich. The single feature can not meet the technical requirements of today. In the aspect of segmentation granularity, the segmentation framework based on pixel level often leads to the incompleteness of the segmentation target, and some segmentation frameworks based on the region exist in detail. For the problem of multi source attribute synergetic segmentation, this paper focuses on the following aspects: first, an effective texture modeling method is proposed. The nonlinear precision is obtained by the traditional multi-scale structure Zhang Liangjin row simplification and nonlinear filtering. The texture descriptor of the texture descriptor used by the nonlinear simplification of multi scale structure tensor and global variation is introduced. The validity of the texture descriptor is tested by the graph cut frame. The texture descriptor is better than the common texture descriptor. The effect, and its lower dimensional characteristics also brings efficiency to the subsequent probabilistic modeling. Secondly, a collaborative segmentation framework based on the multi source attributes of virtual nodes is proposed, and the framework is used for multi feature synergetic segmentation. The L*a*b color feature and the texture modeling method we put forward are based on virtual nodes. By comparing with the traditional color texture energy mixing model and the single feature segmentation result, it shows that the segmentation framework can well absorb the advantages of different feature segmentation. Finally, the cooperative segmentation of multi source attributes based on context information is proposed. The frame is cut and the framework is used in multi granularity cooperative segmentation. The coarse granularity and fine grain size of the homogeneous region obtained by the edge enhanced mean shift algorithm is used as an example to experiment on the coarse and fine granularity cooperative segmentation method based on context information. By comparing the segmentation results based on the single granularity, the segmentation framework is illustrated. Through a large number of simulation experiments, this paper validates the texture modeling method in this paper, the effectiveness and availability of multi granularity cooperative segmentation based on virtual nodes and multi granularity based on context information, and has a good application prospect.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41
本文編號(hào)：2157142
[Abstract]:How to quickly get users interested in the complex scene is becoming a hot and difficult point in the field of computer vision and pattern recognition. As an effective way to extract the target, image segmentation has been widely concerned by scholars and has also taken more research results. There are many difficulties, such as: more and more information is included in the image, and the features of images are becoming more and more rich. The single feature can not meet the technical requirements of today. In the aspect of segmentation granularity, the segmentation framework based on pixel level often leads to the incompleteness of the segmentation target, and some segmentation frameworks based on the region exist in detail. For the problem of multi source attribute synergetic segmentation, this paper focuses on the following aspects: first, an effective texture modeling method is proposed. The nonlinear precision is obtained by the traditional multi-scale structure Zhang Liangjin row simplification and nonlinear filtering. The texture descriptor of the texture descriptor used by the nonlinear simplification of multi scale structure tensor and global variation is introduced. The validity of the texture descriptor is tested by the graph cut frame. The texture descriptor is better than the common texture descriptor. The effect, and its lower dimensional characteristics also brings efficiency to the subsequent probabilistic modeling. Secondly, a collaborative segmentation framework based on the multi source attributes of virtual nodes is proposed, and the framework is used for multi feature synergetic segmentation. The L*a*b color feature and the texture modeling method we put forward are based on virtual nodes. By comparing with the traditional color texture energy mixing model and the single feature segmentation result, it shows that the segmentation framework can well absorb the advantages of different feature segmentation. Finally, the cooperative segmentation of multi source attributes based on context information is proposed. The frame is cut and the framework is used in multi granularity cooperative segmentation. The coarse granularity and fine grain size of the homogeneous region obtained by the edge enhanced mean shift algorithm is used as an example to experiment on the coarse and fine granularity cooperative segmentation method based on context information. By comparing the segmentation results based on the single granularity, the segmentation framework is illustrated. Through a large number of simulation experiments, this paper validates the texture modeling method in this paper, the effectiveness and availability of multi granularity cooperative segmentation based on virtual nodes and multi granularity based on context information, and has a good application prospect.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 韓守東;趙勇;陶文兵;桑農(nóng);;基于高斯超像素的快速Graph Cuts圖像分割方法[J];自動(dòng)化學(xué)報(bào);2011年01期
2 劉麗;匡綱要;;圖像紋理特征提取方法綜述[J];中國(guó)圖象圖形學(xué)報(bào);2009年04期
，本文編號(hào)：2157142
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