立體圖像智能處理關(guān)鍵技術(shù)研究
發(fā)布時(shí)間：2018-07-31 18:01
【摘要】：2009年以來(lái),以阿凡達(dá)為代表的3D電影在全球的興起,帶動(dòng)了立體相機(jī)、立體電視等產(chǎn)業(yè)市場(chǎng)的高速增長(zhǎng),也推動(dòng)了多媒體研究領(lǐng)域中立體視覺(jué)媒體相關(guān)處理技術(shù)研究的進(jìn)步和發(fā)展。其中,立體圖像處理是對(duì)雙目立體格式的圖像進(jìn)行加工處理的技術(shù),是支撐和引領(lǐng)相關(guān)行業(yè)發(fā)展的核心技術(shù)。充分利用立體圖像的雙目特性,提高圖像處理的質(zhì)量和效率,將極大推動(dòng)計(jì)算機(jī)視覺(jué)智能研究的進(jìn)步和立體視覺(jué)消費(fèi)產(chǎn)業(yè)的發(fā)展。同傳統(tǒng)的圖像處理技術(shù)相比,立體圖像處理技術(shù)的關(guān)鍵在于對(duì)雙目左右視角相關(guān)性的挖掘和利用。把傳統(tǒng)圖像處理技術(shù)和視角相關(guān)性高效地融合,才能提高立體圖像的處理質(zhì)量。本文針對(duì)目前立體圖像處理領(lǐng)域的應(yīng)用需求,從分析立體圖像處理的特性入手,對(duì)立體圖像處理中的深度獲取、顯著性分析和交互式對(duì)象分割三個(gè)關(guān)鍵技術(shù)展開(kāi)研究。論文的主要?jiǎng)?chuàng)新和貢獻(xiàn)點(diǎn)包括以下幾個(gè)方面：1.提出了一種新的立體匹配方法,通過(guò)結(jié)合視角相關(guān)性與顏色相似性,有效地解決了現(xiàn)有方法過(guò)度依賴顏色分布而導(dǎo)致的精度不足和魯棒性較差等問(wèn)題�，F(xiàn)有的局部立體匹配方法主要基于單個(gè)視角內(nèi)容的顏色相似性。這類方法過(guò)度依賴于圖像顏色和深度分布的一致性,在實(shí)際應(yīng)用中的精度和魯棒性都不高。本文通過(guò)分析立體圖像左右視角的相關(guān)性,并與單視角的顏色相似性線索相結(jié)合,提出了一種將兩者進(jìn)行互補(bǔ)的代價(jià)聚集方法。該方法不僅提高了立體匹配的精度,在實(shí)際應(yīng)用中也表現(xiàn)出更好的魯棒性。2.提出了一種基于立體視覺(jué)的顯著性分析方法,其檢測(cè)結(jié)果要明顯優(yōu)于現(xiàn)有最好的方法。 目前的視覺(jué)顯著性分析主要以二維平面圖像作為視覺(jué)輸入,未能有效地挖掘場(chǎng)景深度信息的潛力,并且缺少相關(guān)的數(shù)據(jù)集支撐基于深度的顯著性研究。本文以立體圖像作為視覺(jué)輸入,重點(diǎn)討論了利用立體圖像中隱藏的深度信息進(jìn)行顯著對(duì)象檢測(cè)的問(wèn)題。通過(guò)考察深度圖像和顯著對(duì)象空間結(jié)構(gòu)的特性,本文提出了一種基于深度各向異性對(duì)比度的顯著區(qū)域檢測(cè)方法,并與現(xiàn)有的基于二維圖像的方法進(jìn)行了結(jié)合。在實(shí)驗(yàn)評(píng)測(cè)中,本文方法的精度和F值都比現(xiàn)有最好方法高出15%以上。此外,針對(duì)目前缺少基于深度的顯著對(duì)象檢測(cè)數(shù)據(jù)集的現(xiàn)狀,本文公布了目前國(guó)際上最大的基于深度的顯著對(duì)象檢測(cè)數(shù)據(jù)集,以推動(dòng)本領(lǐng)域的相關(guān)研究。3.提出了一種服務(wù)于立體圖像的一致性分割方法,大幅提升了立體圖像左右視角聯(lián)合分割的處理效率�，F(xiàn)有的視角一致性分割方法大多是基于區(qū)域的,沒(méi)有考慮到立體圖像視角內(nèi)容間的相似性,導(dǎo)致計(jì)算冗余度過(guò)高。針對(duì)該問(wèn)題,本文提出了一種基于輪廓的一致性分割方法,其計(jì)算效率達(dá)到現(xiàn)有最快方法的10倍,并且具有更好的一致性分割精度。此外,本文方法采用了對(duì)一致性約束獨(dú)立求解的思路,因而可以與任何現(xiàn)有的單視角分割方法無(wú)縫結(jié)合,共同處理立體圖像分割問(wèn)題。4.提出了一種基于顏色和深度自適應(yīng)融合的對(duì)象分割方法,顯著地提升了對(duì)象分割的精度。 目前的圖像分割方法大多是基于顏色信息的,未能對(duì)深度信息的特性進(jìn)行充分的挖掘。本文通過(guò)考察深度圖像的特點(diǎn),提出了一種基于測(cè)地距離的對(duì)象分類模型,并進(jìn)一步通過(guò)分析深度與顏色信息在圖像分割中各自的特性,對(duì)顏色與深度信息進(jìn)行了自適應(yīng)融合。該方法在兩個(gè)相關(guān)評(píng)測(cè)平臺(tái)上比現(xiàn)有最好方法分別取得了2%和3%的F值提升,并且精度和召回率指標(biāo)都高達(dá)95%以上。在以上關(guān)鍵技術(shù)研究的基礎(chǔ)上,本文還給出了一系列立體圖像智能處理的案例,從而展示了本文研究成果在相關(guān)應(yīng)用領(lǐng)域中具有很好的支撐作用和應(yīng)用前景。
[Abstract]:Since 2009, the rise of 3D films represented by arfan has led to the rapid growth of stereoscopic cameras, stereoscopic television and other industrial markets, which also promoted the progress and development of the research on stereoscopic visual media related processing technology in the field of multimedia research. The processing technology is the core technology to support and lead the development of the related industries. Making full use of the binocular features of stereoscopic images, improving the quality and efficiency of image processing, will greatly promote the progress of computer vision intelligence research and the development of stereoscopic visual consumption industry. The key lies in the mining and utilization of the relativity between the eyes and the eyes. In order to improve the quality of the stereo image processing, the quality of the stereo image processing can be improved by combining the traditional image processing technology with the angle of view, so this paper, aiming at the application requirement of the stereo image processing field, starts with the analysis of the characteristics of the stereo image processing, and the depth acquisition in the stereo image processing. The main innovations and contributions of this paper are as follows: (1.) a new method of stereo matching is proposed, which can effectively solve the accuracy of the over dependent color distribution of the existing method by combining the angle of view with the color similarity. The existing local stereo matching methods are mainly based on the color similarity of the single view content. This method is overly dependent on the consistency of the color and depth distribution of the image, and the accuracy and robustness in the practical application are not high. This paper analyzes the correlation of the angle of view of the stereo images and is with the single angle of view. With the combination of color similarity cues, a method of cost aggregation is proposed to complement each other. This method not only improves the accuracy of stereo matching, but also shows a better robustness in practical application..2. proposes a method of saliency analysis based on stereoscopic vision. Its detection results are obviously superior to the best existing methods. The current visual saliency analysis mainly uses two-dimensional plane images as visual input, fails to effectively excavate the potential of depth information of the scene, and lacks the related data sets to support the significance of depth based research. This paper uses stereoscopic images as visual input, focusing on the use of hidden depth information in stereoscopic images. In this paper, a significant region detection method based on the depth anisotropy contrast is proposed and combined with the existing two dimensional image based method. In the experimental evaluation, the accuracy and F value of this method are better than the existing best methods. In addition, in view of the current lack of a significant object detection data set based on depth, this paper publishes the largest set of significant object detection data based on depth in the world, in order to promote the related research in this field,.3. proposed a method of conformance segmentation for stereoscopic images and greatly enhanced the stereoscopic image. Most of the existing angle of view conformance segmentation methods are based on the region, which does not take into account the similarity between the content of the stereoscopic image, which leads to high computational redundancy. In this paper, a contours based conformance segmentation method is proposed in this paper, and its computational efficiency reaches the fastest method available. 10 times, and has better conformance segmentation precision. In addition, this method adopts the idea of independent solution to the consistency constraint, so it can be combined with any existing single view segmentation method to deal with the problem of stereo image segmentation..4. proposes an object segmentation method based on color color and depth adaptive fusion. Most of the current image segmentation methods are based on the color information and fail to fully excavate the characteristics of the depth information. In this paper, an object classification model based on the distance of the geodesic is proposed by investigating the characteristics of the depth image, and the depth and color information are analyzed in the image. The characteristics of the segmentation are adaptive fusion of color and depth information. The method has obtained 2% and 3% F values on the two related evaluation platform, and the precision and recall index are up to 95%. Based on the key technology research above, a series of stereoscopic images are given. Intelligent processing cases show that the research results of this paper have a good supporting role and application prospects in the related fields.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41
[Abstract]:Since 2009, the rise of 3D films represented by arfan has led to the rapid growth of stereoscopic cameras, stereoscopic television and other industrial markets, which also promoted the progress and development of the research on stereoscopic visual media related processing technology in the field of multimedia research. The processing technology is the core technology to support and lead the development of the related industries. Making full use of the binocular features of stereoscopic images, improving the quality and efficiency of image processing, will greatly promote the progress of computer vision intelligence research and the development of stereoscopic visual consumption industry. The key lies in the mining and utilization of the relativity between the eyes and the eyes. In order to improve the quality of the stereo image processing, the quality of the stereo image processing can be improved by combining the traditional image processing technology with the angle of view, so this paper, aiming at the application requirement of the stereo image processing field, starts with the analysis of the characteristics of the stereo image processing, and the depth acquisition in the stereo image processing. The main innovations and contributions of this paper are as follows: (1.) a new method of stereo matching is proposed, which can effectively solve the accuracy of the over dependent color distribution of the existing method by combining the angle of view with the color similarity. The existing local stereo matching methods are mainly based on the color similarity of the single view content. This method is overly dependent on the consistency of the color and depth distribution of the image, and the accuracy and robustness in the practical application are not high. This paper analyzes the correlation of the angle of view of the stereo images and is with the single angle of view. With the combination of color similarity cues, a method of cost aggregation is proposed to complement each other. This method not only improves the accuracy of stereo matching, but also shows a better robustness in practical application..2. proposes a method of saliency analysis based on stereoscopic vision. Its detection results are obviously superior to the best existing methods. The current visual saliency analysis mainly uses two-dimensional plane images as visual input, fails to effectively excavate the potential of depth information of the scene, and lacks the related data sets to support the significance of depth based research. This paper uses stereoscopic images as visual input, focusing on the use of hidden depth information in stereoscopic images. In this paper, a significant region detection method based on the depth anisotropy contrast is proposed and combined with the existing two dimensional image based method. In the experimental evaluation, the accuracy and F value of this method are better than the existing best methods. In addition, in view of the current lack of a significant object detection data set based on depth, this paper publishes the largest set of significant object detection data based on depth in the world, in order to promote the related research in this field,.3. proposed a method of conformance segmentation for stereoscopic images and greatly enhanced the stereoscopic image. Most of the existing angle of view conformance segmentation methods are based on the region, which does not take into account the similarity between the content of the stereoscopic image, which leads to high computational redundancy. In this paper, a contours based conformance segmentation method is proposed in this paper, and its computational efficiency reaches the fastest method available. 10 times, and has better conformance segmentation precision. In addition, this method adopts the idea of independent solution to the consistency constraint, so it can be combined with any existing single view segmentation method to deal with the problem of stereo image segmentation..4. proposes an object segmentation method based on color color and depth adaptive fusion. Most of the current image segmentation methods are based on the color information and fail to fully excavate the characteristics of the depth information. In this paper, an object classification model based on the distance of the geodesic is proposed by investigating the characteristics of the depth image, and the depth and color information are analyzed in the image. The characteristics of the segmentation are adaptive fusion of color and depth information. The method has obtained 2% and 3% F values on the two related evaluation platform, and the precision and recall index are up to 95%. Based on the key technology research above, a series of stereoscopic images are given. Intelligent processing cases show that the research results of this paper have a good supporting role and application prospects in the related fields.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41
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