【摘要】：雙目圖像深度提取屬于計算機視覺領(lǐng)域。本文研究目的是對一些已有的雙目圖像深度提取算法進(jìn)行改進(jìn)。在局部算法方面,利用SIFT特征組合絕對差值作匹配代價度量在保持效果的同時加快匹配速度;在全局算法方面,利用圖像分割對置信傳播作出效果方面的改進(jìn)。具體的研究內(nèi)容和改進(jìn)方面如下:1.研究雙目圖像提取深度信息的原理,以及立體匹配的匹配代價度量方法和約束條件,約束條件使具有病態(tài)性的立體匹配問題變成理論上可解。在此基礎(chǔ)上,研究基于窗口的匹配代價聚合方法,固定窗口法和自適應(yīng)窗口法的多窗口法,并根據(jù)圖像處理的模板加權(quán)重思想實現(xiàn)固定窗口法的一個改進(jìn),改進(jìn)后效果與多窗口法接近,計算量近似是多窗口法的1/9。2.利用SIFT特征提取加快行傳播法的計算速度。行傳播法是基于顏色相似對圖像作行線分割,再利用行線區(qū)域進(jìn)行聚合的局部算法。原算法的匹代價度量是組合絕對差值和Census變換,因Census變換涉及大量邏輯比較運算而使計算時間長。針對這一點,本文利用SIFT特征提取代替Census變換來改進(jìn)原算法,絕對差值和SIFT特征的組合度量在效果上與原度量接近,但計算時間減少近75%。3.利用圖像分割改進(jìn)置信傳播的效果。置信傳播是基于馬爾可夫隨機場來最小化全局能量函數(shù)的迭代算法。當(dāng)?shù)螖?shù)過多時,視差值平滑過度,造成圖像中視差不連續(xù)區(qū)域效果變差。本文結(jié)合分水嶺分割和Canny邊緣提取阻止圖像邊緣傳播避免平滑過度。迭代20次后,原算法、結(jié)合分水嶺分割和結(jié)合Canny邊緣提取的算法的誤匹配率分別是4.79%、3.86%、3.25%,效果得到提升。
[Abstract]:Depth extraction of binocular images belongs to the field of computer vision. The purpose of this paper is to improve some existing depth extraction algorithms for binocular images. In the aspect of local algorithm, the absolute difference of SIFT feature combination is used to measure the matching cost while maintaining the effect while accelerating the matching speed. In the aspect of global algorithm, the image segmentation is used to improve the effect of confidence propagation. Specific research contents and improvements are as follows: 1. In this paper, the principle of extracting depth information from binocular images is studied, and the matching cost measurement method and constraint conditions for stereo matching are studied. The constraint conditions make the ill-conditioned stereo matching problem theoretically solvable. On this basis, the window-based matching cost aggregation method, fixed window method and adaptive window method of multi-window method are studied, and an improvement of fixed window method is realized according to the idea of template weighting in image processing. The result of the improved method is close to that of the multi-window method, and the calculation amount is approximately 1 / 9.2 of the multi-window method. SIFT feature extraction is used to speed up the calculation of line propagation method. Line propagation method is a local algorithm based on color similarity for line segmentation and aggregation of line regions. The computation cost measure of the original algorithm is the combination absolute difference and Census transform. Because the Census transform involves a large number of logical comparison operations, the computation time is long. In view of this, this paper uses SIFT feature extraction instead of Census transform to improve the original algorithm. The combined measurement of absolute difference and SIFT feature is close to the original measure in effect, but the calculation time is reduced by nearly 75%. Image segmentation is used to improve the effect of confidence propagation. Confidence propagation is an iterative algorithm based on Markov random fields to minimize the global energy function. When the number of iterations is too many, the parallax smoothing is excessive, which causes the disparity discontinuity in the image to become worse. In this paper, we combine watershed segmentation and Canny edge extraction to prevent image edge propagation from smoothing. After 20 iterations, the error matching rate of the original algorithm, which combines watershed segmentation and Canny edge extraction, is 4.79%, 3.86% and 3.25%, respectively.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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