本論文研究目的主要是使用立體視覺去計算出物體在空間中和相機的距離，並利用反投影將影像中的物體在三維空間中重建目標物體的輪廓。在立體視覺中最重要的步驟就是立體匹配，而在立體匹配過程中有兩個關鍵是很重要的精確度和計算時間，所以本文針對這兩部分去進行探討。在演算法的地方，從FNCC匹配換成SSD匹配使計算時間減少了一大半，並且加入了disparity範圍限制除了再加速計算外也減少了誤差的產生。為了提升精確度，在方法中加入濾波和多項式誤差嵌入去提升精確度。本研究嘗試四種不同的濾波和數種不同銳利化程度以及兩種不同矩形的匹配窗口，來探討銳利化程度以及不同矩形的匹配窗口對濾波提升精度的影響。就結論而言，此方法可以大大提升精確度和降低計算時間。

In this thesis, it will be explained how the stereo vision can be used to calculate the distance between an object and a camera. By using a back projection method in 3D space, two existing images are recovered by which to analyze the contour of a target object. Stereo matching is the most important step in stereo vision as it will determine the accuracy and computational time of required to obtain experimental results. This thesis discusses both accuracy and computational time. In terms of an algorithm, the process is changed from FNCC matching to SSD matching. Then, a disparity range limitations is used to reduce both computational time and the number of mismatched points. To further improve the accuracy, a Gaussian filter and polynomial disparity fitting are added. An unsharp mask filter is used in the experiment to sharpen images, the window size is altered, and different filters are used to find the best way to improve the accuracy and speed of stereo matching. In conclusion, propose method reduced both the number of mismatching points and computational time.

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