Stereo Matching with Confidence-Region Decomposition and Processing

Jeong, Young Ju; Kuo, C.-C. Jay

doi:10.1007/s42835-018-00050-4

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In contrast, global optimization commonly produces higher accuracy of disparity maps compare local methods at computational cost disadvantage. [33]- [36] introduced a global approach on of WTA optimization with dynamic programming and [15] Least Square optimization to find the final disparity value. This global approach produces less errors caused by textureless, occlusion, and discontinuity regions.…”

Section: Figure 1: Traditional Stereo Matching Taxonomy By Scharstein and Szeliskimentioning

confidence: 99%

mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

Kadmin¹,

Hamzah²,

Manap³

et al. 2021

IJETAE

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Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

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Section: Figure 1: Traditional Stereo Matching Taxonomy By Scharstein and Szeliskimentioning

confidence: 99%

mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

Kadmin¹,

Hamzah²,

Manap³

et al. 2021

IJETAE

View full text Add to dashboard Cite

show abstract

Multi-step stereo matching algorithm based on image fragment and confidence propagation

Deng

Zhong

2022

2022 7th International Conference on Communication, Image and Signal Processing (CCISP)

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Multi-brightness layers with a genetic optimization algorithm for stereo matching under dramatic illumination changes

Yang

Qiu

2021

Appl. Opt.

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Stereo matching under dramatic illumination changes is a big challenge in imbalanced binocular vision, self-driving cars, and the remote sensing image field. A novel, to the best of our knowledge, multi-brightness layer mechanism with a genetic optimization algorithm is proposed in this paper. The mechanism of multi-brightness layers transforms the two images with dramatic illumination changes into a series of matched pairs with similar brightness by the stretching function and histogram matching principle. Therefore, the large illumination variations are reduced greatly. Moreover, the initial disparities as first generation of genetic optimization approach are generated from matched pairs using fast segmentation local stereo matching to increase the efficiency and accuracy. For further improving the accuracy of disparity, an enhanced genetic optimization algorithm for stereo matching is designed to have more inliers and continuity. The experimental results comparing with state-of-the-art stereo matching methods demonstrate that the proposed method has better performance in accuracy and stability.

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Stereo Matching with Confidence-Region Decomposition and Processing

Cited by 3 publications

References 13 publications

mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

Multi-step stereo matching algorithm based on image fragment and confidence propagation

Multi-brightness layers with a genetic optimization algorithm for stereo matching under dramatic illumination changes

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