本文選題：計(jì)算機(jī)視覺(jué) + 前景提取��； 參考：《山東大學(xué)》2017年碩士論文

【摘要】：隨著計(jì)算機(jī)視覺(jué)技術(shù)的快速發(fā)展,其成果如增強(qiáng)現(xiàn)實(shí)、虛擬現(xiàn)實(shí)、智能監(jiān)控以及特征識(shí)別等被越來(lái)越多的用于實(shí)際生活中,為人們的生活帶來(lái)了極大的樂(lè)趣與便捷。對(duì)于許多計(jì)算機(jī)視覺(jué)應(yīng)用而言,場(chǎng)景中提取出的前景物體是應(yīng)用進(jìn)行處理的基礎(chǔ)。因此,對(duì)圖像中前景物體的分割提取越來(lái)越受到數(shù)字圖像處理研究者們的重視。盡管目前研究者們?cè)谇熬拔矬w分割方面提出了大量巧妙的算法,但是仍然存在不少問(wèn)題,如提取的前景物體輪廓不準(zhǔn)確,無(wú)法給出獨(dú)立個(gè)體的分割結(jié)果等。隨著Kinect深度相機(jī)等廉價(jià)深度信息獲取設(shè)備的出現(xiàn),結(jié)合色彩和深度信息為提取前景物體提供了一條新的途徑。針對(duì)以上存在的問(wèn)題,本文提出了一種基于圖和深度分層的室內(nèi)場(chǎng)景前景物體提取算法。論文首先對(duì)圖像中前景物體提取的研究背景、意義、國(guó)內(nèi)外研究現(xiàn)狀以及提取前景物體所面臨的難點(diǎn)進(jìn)行了綜述,并介紹了論文的結(jié)構(gòu)安排。然后對(duì)本文中提出的前景物體提取算法中涉及的關(guān)鍵技術(shù)進(jìn)行了詳細(xì)的闡述。最后對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行分析,并進(jìn)一步用本文中算法得到的提取結(jié)果與其他先進(jìn)算法進(jìn)行對(duì)比評(píng)估算法的性能。本文通過(guò)將色彩信息與深度信息進(jìn)行融合以改進(jìn)基于圖的圖像分割算法,對(duì)給予的場(chǎng)景進(jìn)行過(guò)分割用于后續(xù)的合并步驟。然后提出了一種深度圖修復(fù)算法用以修復(fù)給定深度圖中的空洞,基于室內(nèi)場(chǎng)景中的前景物體在深度上起伏較小的假設(shè),利用擴(kuò)展的多閾值大津法將對(duì)應(yīng)的深度圖進(jìn)行分層處理,斷開各前景物體之間以及前景和背景之間在深度上的連續(xù)性。并提出了一種利用種子點(diǎn)自動(dòng)選取合適深度分層的方案以減少交互操作。接著為了解決當(dāng)場(chǎng)景中前景和背景的色彩和深度均相似時(shí)無(wú)法在兩者之間生成有效邊界的難點(diǎn)問(wèn)題,同時(shí)也為了優(yōu)化提取結(jié)果,本文利用深度信息生成法向圖以提供一種新的約束條件。最后,利用深度分層、法向圖、用戶設(shè)置的種子點(diǎn)以及區(qū)域面積設(shè)計(jì)約束規(guī)則對(duì)過(guò)分割的場(chǎng)景圖像進(jìn)行區(qū)域合并,從而在色彩圖和深度圖中提取出具有清晰輪廓的前景物體。論文作者對(duì)算法的實(shí)驗(yàn)結(jié)果進(jìn)行了統(tǒng)計(jì)分析并給出分析結(jié)果,同時(shí)進(jìn)一步將實(shí)驗(yàn)結(jié)果與多種已發(fā)表的不同算法的實(shí)驗(yàn)結(jié)果進(jìn)行對(duì)比。實(shí)驗(yàn)證明,本文算法更加穩(wěn)定,所提取的前景物體更加完整,輪廓更加簡(jiǎn)潔清晰且用戶交互更少。
[Abstract]:With the rapid development of computer vision technology, its achievements, such as augmented reality, virtual reality, intelligent monitoring and feature recognition, are more and more used in real life, which brings great pleasure and convenience to people's life. For many computer vision applications, foreground objects extracted from the scene are the basis of processing. Therefore, the segmentation and extraction of foreground objects in images are paid more and more attention by digital image processing researchers. Although researchers have put forward a large number of clever algorithms in foreground object segmentation, there are still many problems, such as the inaccuracy of the extracted foreground object contour, the inability to give the segmentation results of independent individuals and so on. With the appearance of cheap depth information acquisition equipment such as Kinect depth camera, combining color and depth information provides a new way to extract foreground objects. In view of the above problems, this paper proposes a foreground object extraction algorithm for indoor scene based on graph and depth stratification. Firstly, the background and significance of foreground object extraction in image, the research status at home and abroad and the difficulties in extracting foreground object are summarized, and the structure of the paper is introduced. Then, the key technology of foreground object extraction algorithm proposed in this paper is described in detail. Finally, the experimental results are analyzed and compared with other advanced algorithms to evaluate the performance of the algorithm. In this paper, the color information and depth information are fused to improve the graph-based image segmentation algorithm, and the given scene is over-segmented for subsequent merging steps. Then a depth map repair algorithm is proposed to repair the holes in the given depth map. Based on the assumption that the foreground object in the indoor scene has a small depth fluctuation, the extended multi-threshold method is used to delaminate the corresponding depth map. Disconnect the continuity of depth between foreground objects and between foreground and background. A scheme of selecting suitable depth layer automatically by using seed points is proposed to reduce the interactive operation. Then, in order to solve the problem that the color and depth of background and foreground in the scene are similar, which can not generate the effective boundary between them, and to optimize the extraction results, In this paper, we use depth information to generate normal graphs to provide a new constraint condition. Finally, the over-segmented scene images are merged by depth stratification, normal graph, user-set seed points and area design constraint rules, and the foreground objects with clear contours are extracted from the color map and depth map. In this paper, the experimental results of the algorithm are statistically analyzed and analyzed, and the experimental results are compared with the experimental results of various published algorithms. Experimental results show that the proposed algorithm is more stable, the extracted foreground objects are more complete, the contour is more concise and clear, and the user interaction is less.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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本文編號(hào)：1914972