摘要
提出一种结合SURF算子和极线约束的立体匹配方法。对采集的双目视觉柑橘图像进行R-B分量的计算,在该分量上,采用快速hessian检测子进行特征点检测,并使用SURF描述子对检测到的特征点进行64维的特征描述。采用欧式距离和极线约束进行特征点匹配。实验表明,该方法对一幅图像对的平均处理时间为293ms,在果实被遮挡或光线变化的情况下均能较好地进行特征点提取和匹配。该方法为后续的深度信息计算提供了基础。
A method for stereo matching of binocular citrus images based on SURF operator and epipolar constraint is presented.R-B space images are achieved by linear transform of original images’ color space.Fast Hessian-matrix detector is used to detect the interest points and the SURF descriptor is used to describe the points’ features.Euclidean distance and the epipolar constraint are used for features matching.Experiment shows that the average execution time of the over-all algorithms（including the feature extraction and the feature matching） is 293 ms.The method can detect and match interest points accurately without the influence of citrus being occluded.Once stereo matching work is done successfully,the following computation of depth information of the fruits can be applied.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第8期157-160,共4页
Computer Engineering and Applications
基金
华中农业大学博士科研启动基金(No.52204-09100)
