隨著科技的發展，電腦視覺系統已逐漸被應用到日常生活中，不論是3D電影、障礙物閃避、人臉偵測等等都可以看到立體視覺的應用。一般電腦視覺都是利用兩個攝影機針對同一個場景來取得兩張影像，接著再藉由兩張影像中周圍場景內物體的遠近找出相對關係。本論文提出基於Speed Up Robust Feature(SURF) feature-based 的方法找出物體相對之間的深度訊息。首先將兩張未校正影像經過影像處理後，對影像進行分割，分割出各個物體大略的所在位置。再利用SURF對物體進行匹配的動作，找出物體相對之間的距離。另外我們也重新修正原本三角關係中的公式，使量測出來的距離更為精準。

Humans have the ability to roughly estimate the distance of objects because of stereo vision of the human’s eyes. In this thesis we propose an improved stereo vision system to accurately measure the distance of objects in real world. Object distance is very useful for obstacle avoidance and navigation of autonomous vehicles. The object measurement of stereo camera is better than a single camera, and the theory was proposed in many previous researches. Recent researches have used stereo cameras for different applications such as 3D image construction, distance measurement, and occlusion detection. The proposed measurement procedure is a three-phase process: object detection, segmentation and distance calculation. In distance calculation, we proposed a new algorithm to reduce the error. The result shows our measurement system is capable of providing objects distance with less than 5% measurement error.

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