Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm

With the continuous advancement of industrialization and intelligentization, stereo-vision-based measurement technology for large-scale components has become a prominent research focus. To address weak-textured regions in large-scale component images and reduce mismatches in stereo matching, we prop...

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Main Authors: Nan Chen, Dongri Shan, Peng Zhang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/15/11/5837
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author Nan Chen
Dongri Shan
Peng Zhang
author_facet Nan Chen
Dongri Shan
Peng Zhang
author_sort Nan Chen
collection DOAJ
description With the continuous advancement of industrialization and intelligentization, stereo-vision-based measurement technology for large-scale components has become a prominent research focus. To address weak-textured regions in large-scale component images and reduce mismatches in stereo matching, we propose a cross-scale multi-feature stereo matching algorithm. In the cost-computation stage, the sum of absolute differences (SAD), census, and modified census cost aggregation are employed as cost-calculation methods. During the cost-aggregation phase, cross-scale theory is introduced to fuse multi-scale cost volumes using distinct aggregation parameters through a cross-scale framework. Experimental results on both benchmark and real-world datasets demonstrate that the enhanced algorithm achieves an average mismatch rate of 12.25%, exhibiting superior robustness compared to conventional census transform and semi-global matching (SGM) algorithms.
format Article
id doaj-art-b0003ced987441e5aa5f40ddd9493b01
institution DOAJ
issn 2076-3417
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publishDate 2025-05-01
publisher MDPI AG
record_format Article
series Applied Sciences
spelling doaj-art-b0003ced987441e5aa5f40ddd9493b012025-08-20T03:10:54ZengMDPI AGApplied Sciences2076-34172025-05-011511583710.3390/app15115837Improvement of the Cross-Scale Multi-Feature Stereo Matching AlgorithmNan Chen0Dongri Shan1Peng Zhang2School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, ChinaSchool of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, ChinaSchool of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, ChinaWith the continuous advancement of industrialization and intelligentization, stereo-vision-based measurement technology for large-scale components has become a prominent research focus. To address weak-textured regions in large-scale component images and reduce mismatches in stereo matching, we propose a cross-scale multi-feature stereo matching algorithm. In the cost-computation stage, the sum of absolute differences (SAD), census, and modified census cost aggregation are employed as cost-calculation methods. During the cost-aggregation phase, cross-scale theory is introduced to fuse multi-scale cost volumes using distinct aggregation parameters through a cross-scale framework. Experimental results on both benchmark and real-world datasets demonstrate that the enhanced algorithm achieves an average mismatch rate of 12.25%, exhibiting superior robustness compared to conventional census transform and semi-global matching (SGM) algorithms.https://www.mdpi.com/2076-3417/15/11/5837stereo matchingcross-scale cost fusionmachine vision
spellingShingle Nan Chen
Dongri Shan
Peng Zhang
Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
Applied Sciences
stereo matching
cross-scale cost fusion
machine vision
title Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
title_full Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
title_fullStr Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
title_full_unstemmed Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
title_short Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
title_sort improvement of the cross scale multi feature stereo matching algorithm
topic stereo matching
cross-scale cost fusion
machine vision
url https://www.mdpi.com/2076-3417/15/11/5837
work_keys_str_mv AT nanchen improvementofthecrossscalemultifeaturestereomatchingalgorithm
AT dongrishan improvementofthecrossscalemultifeaturestereomatchingalgorithm
AT pengzhang improvementofthecrossscalemultifeaturestereomatchingalgorithm