立体匹配论文：汽车辅助驾驶系统中立体匹配算法研究.doc

立体匹配论文：汽车辅助驾驶系统中立体匹配算法研究【中文摘要】立体匹配是计算机视觉体系中的重要组成部分,在基于立体视觉的驾驶辅助系统中更是起到核心作用。驾驶辅助系统是一种能起到主动预警作用的车辆驾驶安全系统,车辆中安装两台摄像机模拟人的视觉系统,利用获取的同一时刻图像对进行立体匹配,得到视差图进而得到深度图,从而获取场景的三维信息,相对于雷达、激光等技术,基于双目视觉的驾驶辅助系统为驾驶员提供了更丰富的路况信息,能够主动预警障碍物并识别路标信息。在辅助驾驶系统中,车辆运行速度快,路面环境复杂,且噪声干扰较多,因此找到一种速度快,且对复杂场景、噪声等情况鲁棒性好的匹配算法是关键问题。基于图像分割的立体匹配算法利用原始图像中平滑区域也是视差平滑的区域视差不连续区域一般也是分割边缘的特性,首先对图像利用均值偏移算法进行分割,然后利用分割信息拟合由速度较快的窗口法得到的初始视差,得到最终视差图。这种方法对于复杂的路面信息和噪声干扰都有很好的适应性,本文对这种算法进行了深入的研究,主要工作如下：在深入分析基于图像分割的立体匹配算法的基础上,在初始视差求取中结合像素灰度差绝对值之和(SAD)算法和基于梯度的(GRAD)算法,并利用赢者全赢算法对左右图分别求得的视差图进行互检测求得更准确的初始视差图,并选择-种自适应阈值的基于图论的图像分割方法获取原始图像的分割信息,这种基于新的分割方法的立体匹配不但可以获得比较准确的深度图且达到了提高时间效率的。结果表明,在室外场景的实验中,对于光照变化其他噪声,算法也具有很好的鲁棒性,能准确获取目标深度。最后,对所做研究的结果进行了总结和分析,并提出了将来需要改进的方向。【英文摘要】Stereo matching is a very important component in computer vision and also plays a central role in stereo vision based Driver Assistance System. Driver assistance system is an early warning vehicle security system which can play a proactive role in driving. With two cameras installed in the vehicle to simulate the human visual system, system uses images obtained in the same time to run stereo matching to get disparity map. Then depth map can be obtained, which can help to recovery three-dimensional scene information. Opposed to radar, laser technology, stereo vision based driver assistance system provide drivers richer traffic information for initiative and early warning obstacle and identifying signs information.In Driver Assistance Systems, the vehicle is in high speed and the road environment is complex with much noise, so it is very important to find a matching method with high speed and good robustness to complex scenes and noise, etc. Image segmentation based stereo matching algorithm consider that smooth region in the original image always have smooth disparity and disparity discontinuities are generally appear with edge features. By using mean-shift image segmentation, this method get segmentation information and use them to fit initial disparity, which is got by fast window method, to get final disparity map. This method have good adaptability for complex information and road noise, this paper have in-depth research in this algorithm, mainly as follows: After in-depth analysis of image segmentation based stereo matching algorithm, in this paper, the initial disparity is computed by the combination of sum of absolute difference(SAD) algorithm and gradient (GRAD) algorithm. And the use of winner-win Strategy (with disparity obtained from the left and right respectively) can help to obtain more accurate initial disparity map. In the meantime, an adaptive threshold graph based image segmentation method is chosen to obtain the original information. The new segmentation-based stereo matching method not only get accurate depth map but also improve time efficiency. And experiment results show that the algorithm also has good robustness for the illumination or other noise in outdoor scenes, which can obtain the target depth accurately.Finally, the summary of our work is given and future direction for improvement is proposed.【关键词】立体匹配 驾驶辅助系统 图像分割 图论【英文关键词】stereo matching Driver Assistance System graph image segmentation【目录】汽车辅助驾驶系统中立体匹配算法研究摘要8-9ABSTRACT9缩略语11-12第一章 绪论12-161.1 研究背景与选题意义12-131.2 国内外研究现状13-151.3 本文研究内容与主要工作15-16第二章 摄像机标定与图像校准16-232.1 摄像机标定16-202.1.1 单目相机标定16-182.1.2 双目摄像机标定18-202.2 立体图像对极线校准20-23第三章 立体匹配算法概述23-353.1 双目立体视觉基本原理23-253.2 立体匹配的基本原理和约束条件25-263.3 立体匹配算法简介26-313.3.1 基于特征的立体匹配方法273.3.2 基于区域的立体匹配方法27-313.4 基于图像分割的立体匹配算法概述31-333.5 本章小结33-35第四章 图像分割算法概述35-404.1 图像分割方法分类35-384.1.1 基于阈值分割的方法354.1.2 基于特征空间聚类的方法35-374.1.3 基于图论的图像分割算法37-384.2 自适应阈值的基于图论的图像分割方法38-404.2.1