基於四維光場之雜訊抑制深度估測方法

隨著科技的日趨成熟，以及人們對於所處環境的認知需求，有關三維環境之場景深度估測，已然成為高度熱門的研究主題之一。近年來，隨著市場需求乃至於工業發展的興起，場景深度估測的研究領域更是擴展到了新的應用層面上，如民生需求的虛擬實境、機器人導航，以及工業生產線上品質管理的瑕疵檢測等。因此，本論文將使用影像處理技術，利用影像的二維空間資訊，發展出一套演算法，藉以偵測出景物之三維深度資訊。本論文進行了三維場景表面重建之相關的文獻回顧，包括立體視覺以及光場攝影這兩個目前較為主流且基於影像的深度估測方式，最後以光場攝影為基礎，提出了一套場景深度估測方法。利用光場相機的四維光場資訊，擷取出一般傳統相機所無法獲得之不同視角的光線資訊，透過座標轉換，將能取得一系列的sub-aperture images以及 epipolar plane images。本論文所提之演算法結合了EPI分析與馬可夫隨機場(Markov Random Fields)之影像分割技術，並利用所設計的去雜訊流程來獲取深度資訊。最後，將本演算法應用於具有真實深度資訊以及其他在各種不同環境條件下之光場來源進行測試，根據均方誤差(MSE)之計算，並與其他演算法做比較，能夠顯示出本演算法擁有較佳的準確度與執行效率，亦能獲得與真實場景較為一致之深度結果。

關鍵字

深度估測；光場； EPI分析；影像分割

並列摘要

With the progress of science and technology and people’s need for cognition about their surroundings, depth estimation has been a highly popular research topic for decades. In recent years, with the rise of market demand and industrial development, the research of depth estimation has even been extended to a variety of applications, such as visual reality, robot navigation, and the defect detection in the industrial production lines. In this thesis, the two dimensional spatial information of a sequence of images by image processing technology is utilized to develop a depth estimation algorithm. The related studies of depth estimation are reviewed, including stereo vision and light field photography, the two main image-based depth estimation methods. This thesis focuses on the light field photography method. The four-dimensional light fields that are acquired from plenoptic camera can record much more light information coming from different directions, but the traditional camera can just contain a part of the light information. Via transformations, a sequence of images called sub-aperture images and epipolar plane images can be obtained. we use the 4D light field data to propose a depth estimation algorithm, combining epipolar plane image analysis and our denoising processing based on Markov Random Fields and LoG filter. In the results, it is shown that our proposal can obtain the consistent depth with the scene with higher accuracy and efficiency.

並列關鍵字

depth estimation ； light fields ； EPI analysis ； image segmentation

參考文獻

[3] Metta, Giorgio, Antonios Gasteratos, and Gulio Sandini. "Learning to track colored objects with log-polar vision." Mechatronics 14.9 (2004): 989-1006.

[4] Manzotti, Riccardo, et al. "Disparity estimation on log-polar images and vergence control." Computer Vision and Image Understanding 83.2 (2001): 97-117.

[5] Barnard, Stephen T., and William B. Thompson. "Disparity analysis of images."IEEE Transactions on Pattern Analysis and Machine Intelligence 4 (1980): 333-340.

[7] Mühlmann, Karsten, et al. "Calculating dense disparity maps from color stereo images, an efficient implementation." International Journal of Computer Vision47.1-3 (2002): 79-88.

[8] Di Stefano, Luigi, Massimiliano Marchionni, and Stefano Mattoccia. "A fast area-based stereo matching algorithm." Image and vision computing 22.12 (2004): 983-1005.

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基於四維光場之雜訊抑制深度估測方法

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