Stereo Matching based on Pyramid transform Cross-Scale Cost Aggregation

Authors

Yao Li, 1. Key Laboratory of Computer Network and Information Integration (Southeast University), Nanjing 211189, China;;2. State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing 210032, China;;3. School of Computer Science and Engineering, Southeast University, Nanjing 211189, China;
Zhukui Liu, 3. School of Computer Science and Engineering, Southeast University, Nanjing 211189, China;
Bingfeng Wang, 3. School of Computer Science and Engineering, Southeast University, Nanjing 211189, China;

Abstract

Abstract: Human visual system processes the received visual signals on different scales, however, the traditional stereo matching algorithms get the disparity map from the original image in the biggest scale, which will lead to high stereo matching error rate in the area of low-texture, texture-less area. Simulation of the human visual system in stereo matching on multiple scales can reduce error rate. A stereo matching method based on the Gaussian pyramid cross-scale transform was improved, by adding the Laplace Pyramid Transform to the original cross-scale framework and adding the weighted joint bilateral filter in the disparity refinement stage. The new cross-scale framework can get a better disparity map than the original method.

Recommended Citation

Li, Yao; Liu, Zhukui; and Wang, Bingfeng (2020) "Stereo Matching based on Pyramid transform Cross-Scale Cost Aggregation," Journal of System Simulation: Vol. 28: Iss. 9, Article 44.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss9/44

First Page

2227

Revised Date

2016-07-14

DOI Link

https://doi.org/

Last Page

2234

CLC

TP391.4

Recommended Citation

Yao Li, Liu Zhukui, Wang Bingfeng. Stereo Matching based on Pyramid transform Cross-Scale Cost Aggregation[J]. Journal of System Simulation, 2016, 28(9): 2227-2234.