人工智能
计算机科学
匹配(统计)
棱锥(几何)
卷积神经网络
模式识别(心理学)
比例(比率)
计算机视觉
特征(语言学)
图形
融合
Blossom算法
算法
数学
语言学
统计
物理
几何学
哲学
理论计算机科学
量子力学
作者
Xing Chen,Wen‐Hai Zhang,Yu Hou,Yang Lin
标识
DOI:10.1051/jnwpu/20213940876
摘要
Aiming at the low matching accuracy of local stereo matching algorithm in weak texture or discontinuous disparity areas, a stereo matching algorithm combining multi-scale fusion of convolutional neural network (CNN) and feature pyramid structure (FPN) is proposed. The feature pyramid is applied on the basis of the convolutional neural network to realize the multi-scale feature extraction and fusion of the image, which improves the matching similarity of the image blocks. The guide graph filter is used to quickly and effectively complete the cost aggregation. The disparity selection stage adapts the improvement dynamic programming algorithm to obtain the initial disparity map. The initial disparity map is refined so as to obtain the final disparity map. The algorithm is trained and tested on the image provided by Middlebury data set, and the result shows that the disparity map obtained by the algorithm has good effect.
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