Adaptive Weight Based Sparse Block Aggregation Algorithm for Stereo Matching

Author(s):  
Cheng Han ◽  
Wen Liu ◽  
Longbin Jin ◽  
Shan Jiang ◽  
Hua Li
2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Hui Li ◽  
Xiao-Guang Zhang ◽  
Zheng Sun

In traditional adaptive-weight stereo matching, the rectangular shaped support region requires excess memory consumption and time. We propose a novel line-based stereo matching algorithm for obtaining a more accurate disparity map with low computation complexity. This algorithm can be divided into two steps: disparity map initialization and disparity map refinement. In the initialization step, a new adaptive-weight model based on the linear support region is put forward for cost aggregation. In this model, the neural network is used to evaluate the spatial proximity, and the mean-shift segmentation method is used to improve the accuracy of color similarity; the Birchfield pixel dissimilarity function and the census transform are adopted to establish the dissimilarity measurement function. Then the initial disparity map is obtained by loopy belief propagation. In the refinement step, the disparity map is optimized by iterative left-right consistency checking method and segmentation voting method. The parameter values involved in this algorithm are determined with many simulation experiments to further improve the matching effect. Simulation results indicate that this new matching method performs well on standard stereo benchmarks and running time of our algorithm is remarkably lower than that of algorithm with rectangle-shaped support region.


Author(s):  
Jilong Bian ◽  
Jinfeng Li ◽  
Nana Wang ◽  
Liu Yang

Author(s):  
Weidong Hu ◽  
Kang Zhang ◽  
Lifeng Sun ◽  
Jiyang Li ◽  
Yijing Li ◽  
...  

2009 ◽  
Vol 29 (10) ◽  
pp. 2690-2692
Author(s):  
Bao-hai YANG ◽  
Xiao-li LIU ◽  
Dai-feng ZHA

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