於雙眼建築場景中保留透視圖法及視差特性的影像修補方法

Perspective and Parallax Preserved Inpainting for Stereoscopic Architectural Scene Images

陸品樺

指導教授 ： 林奕成

國立交通大學/資訊學院/多媒體工程研究所/碩士(2011年)

https://doi.org/10.6842/NCTU.2011.00962

摘要

對於移除非預期出現的前景障礙物體，影像完成技術是一個強而有力的方法。然而，當障礙物體遮蔽大範圍的背景且背景為人造結構物時，背景的結構性是很難以維持的。這篇論文我們提出了一個對雙眼建築場景影像的自動化影像完成方法。我們利用了雙視角的資訊來降低障礙物像素並且維持雙眼視覺的一致性。我們利用一個消失點和消失線的偵測方法來將輸入影像做透視校正以減少透視瑕疵。並且我們也提出了一個結構增強的補片搜尋方法來維持建築物的結構性。我們所提出的方法將試驗於幾組不同的人造結構物影像上以展示合理的結果。

關鍵字

透視結構分析 ； 影像完成

並列摘要

Image completion algorithms are powerful for removing undesirable obstacle objects. Nevertheless, when the undesirable obstacle objects cover a large amount of the background and the background is artificial constructed, structure of the background is difficult to maintain. In this thesis, we present an automatic image completion method for stereoscopic architectural scene images. We take advantage of two-view information to reduce obstacle pixels and preserve parallax consistency. A vanishing point and vanishing line prediction is used to perspective correct the input images to eliminate perspective artifacts. A structure-enhanced patch searching algorithm is also proposed to preserve the architectural structure. The proposed method is performed on a number of artificial construction images to show reasonable results.

並列關鍵字

Perspective structure analysis ； image completion

參考文獻

[ADV03] A. Almansa,A. Desolneux, S. Vamech. “Vanishing point detection without any a priori information,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, Issue 4, pp. 502-507, 2003.

[BAR83] S. T. Barnard, “Interpreting perspective images,” Artificial Intelligence 21, pp.435-462, 1983.

[BBS01] M. Bertalmio, A. L. Bertozzi, G. Sapiro, “Navier-stokes, fluid dynamics, and image and video inpainting,” Computer Vision and Pattern Recognition, Vol. 1, pp. 355-362, 2001.

[BVS03] M. Bertalmio, L. Vese, G. Sapiro, S. Osher, “Simultaneous structure and texture image inpainting,” IEEE Transactions on Image Processing, Vol. 12, no. 8, pp. 882-889, 2003.

[CAN86] J. Canny, “A computational approach to edge detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679–698, 1986.