| 引用本文: | 闫小宇,陆凡凡,葛芦生,伍孟涛,刘 彬.基于双目立体视觉系统的测量研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2023，40（3）：48-55 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 为了实现摄像机与目标物体之间距离的信息,由双目测量原理,采取结合OpenCV与Matlab的方式,设计出一套关于双目测距的立体视觉系统;系统首先对双目摄像机的内外参数进行标定,从黑白格组成的标定板中获得角点信息,使用亚像素角点检测法对角点坐标信息进行更精确检测,在黑白格组成的标定板分别距离双目摄像机300、400、500、600、700mm处获取不同位置的标定图像,经过张正友标定法最终可以得到双目摄像机所需内外参数;其次通过BM(Block Matching)立体匹配算法在VS2017坏境与opencv3.4.7库配合下完成了摄像机的立体校正、立体匹配进而得到视差图;最后在实验中使用了双目摄像头,并编写了代码通过鼠标点击所得到的视差图获取对应的世界坐标来实现物距的测量;实验结果表明:被测物距离摄像头光心500~700mm这一范围时,实测距离和实际距离相对误差百分比在0.171% ~0.192%之间,且实测距离在2 950mm内实验误差小于5%满足实验精度要求。 |
| 关键词: 计算机视觉 双目测距 块匹配算法 摄像机标定 |
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| Image Style Transfer Based on the Distribution Matching of the Style Features |
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YAN Xiaoyu, LU Fanfan, GE Lusheng, WU Mengtao, LIU Bin
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School of Electrical and Information Engineering, Anhui University of Technology, Anhui Ma'anshan 243002, China
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| Abstract: |
| In order to obtain the information of the distance between the camera and the target object, based on the principle of binocular measurement, a stereo vision system for binocular ranging was designed by combining OpenCV and Matlab. This system first calibrated the internal and external parameters of the binocular camera, got the corner point information from the calibration board composed of black and white grids, and used the sub-pixel corner detection method
to detect the corner point coordinate information more accurately. The board was respectively 300mm, 400mm, 500mm, 600mm, and 700mm away from the binocular camera to obtain calibration images at different positions. By using Zhang Zhengyou's calibration method, the internal and external parameters required by the binocular camera were finally obtained. Secondly, through the BM(Block Matching) stereo matching algorithm in the VS2017 environment and the opencv3.4.7 library, the stereo correction and stereo matching of the camera were completed to obtain the disparity map. Finally, a binocular camera was used in the experiment, and a code was written to obtain the corresponding world coordinates through the disparity map obtained by clicking the mouse to realize the measurement of the object distance. The experimental results show that when the measured object is in the range of 500 mm to 700 mm from the optical center of the camera, the relative error percentage between the measured distance and the actual distance is between 0.171% and 0.192%, and the accuracy is high and when the measured distance is within 2 950 mm and the experimental error is less than 5%, which meets the requirements of experimental accuracy. |
| Key words: computer vision binocular ranging block matching algorithm camera calibration |
