摘要
针对局部立体匹配方法存在的匹配窗口大小选择困难、弱纹理或高光区域立体匹配精度较低等问题,文中结合卷积神经网络(CNN)与图像金字塔方法,提出多尺度融合的立体匹配算法.训练CNN,用于自动学习待匹配图像对的图像特征,完成匹配代价计算.构建图像金字塔,对待匹配图像对进行多尺度表达.构建弱纹理区域模板,将各层待匹配图像划分为弱纹理区域和丰富纹理区域,将弱纹理区域图像变换成小尺度图像进行匹配度计算,降低弱纹理图像的误匹配率.在变换回大尺度图像时与丰富纹理区域匹配结果融合,保持匹配精度.在KITTI数据集上的实验表明,文中算法具有较好的图像匹配效果.
Aiming at the problems of local stereo matching methods,such as difficulty in selecting sizes of matching windows and low accuracy of stereo matching in weak texture or highlight region,a multi-scale fusion stereo matching method is proposed by combining convolutional neural network model(CNN)and image pyramid method in this paper.By training CNN,image features of the matched image pairs are learned automatically to complete the calculation of matching cost.Based on the construction of image pyramids,the matched image pairs are expressed in multiple scale.Grounded on the template construction of weak texture region,the matching images of each layer are divided into weak texture region and rich texture region.The image of weak texture region is transformed into small-scale image to calculate the matching degree and reduce the mismatching rate of weak texture image.Then,the image is transformed back to large-scale images and fused with the matching results of rich texture regions to maintain the matching accuracy.Experiments on KITTI dataset indicate that the proposed algorithm yields a better image matching result.
作者
徐雪松
吴俊杰
XU Xuesong;WU Junjie(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2020年第2期182-187,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61763012)资助。
