Sparse representation over discriminative dictionary for stereo matching
作者：
Highlights：
• Presenting a novel data-driven matching cost function based on sparse coding.
• Learning a dictionary by using weighted sparse coding and discriminative learning.
• The number of atoms in the dictionary is automatically decided during training.
• Computing the matching costs based on the sparse representations.
• The proposed method achieves the best accuracy on 30 test stereo images.
摘要
•Presenting a novel data-driven matching cost function based on sparse coding.•Learning a dictionary by using weighted sparse coding and discriminative learning.•The number of atoms in the dictionary is automatically decided during training.•Computing the matching costs based on the sparse representations.•The proposed method achieves the best accuracy on 30 test stereo images.
论文关键词：Computer vision,Stereo matching,Data-driven,Sparse coding,Dictionary learning
论文评审过程：Received 28 December 2016, Revised 31 May 2017, Accepted 7 June 2017, Available online 10 June 2017, Version of Record 21 June 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.06.015
