Real‐time multi‐window stereo matching algorithm with fuzzy logic

Vázquez-Delgado, H. D.; Pérez-Patricio, Madaín; Aguilar-González, Abiel; Arias-Estrada, Miguel; Palacios-Ramos, M. A.; Camas-Anzueto, J.L.; Pérez-Cruz, Antonio; Velázquez-Trujillo, Sabino

doi:10.1049/cvi2.12031

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…Performance of the FGS algorithm was compared with 12 recently developed non-learning-based disparity estimation procedures including 7 local methods, 3 global methods, and one fusion method using both local and global approaches for disparity estimation. The local methods were: weighted adaptive cross-region-based guided image filtering method (ACR-GIF-OW) 54 , real-time stereo matching algorithm with FPGA architecture (MANE) 55 , adaptive support-weight approach in pyramid structure (DAWA-F) 56 , encoding-based approaches PPEP-GF 57 , absolute difference (AD) and census transform-based stereo matching with guided image filtering (ADSR-GIF) 58 , the sum of absolute difference (SAD) based stereo matching aggregated with adaptive weighted bilateral filter (SM-AWP) 59 , statistical maximum a posteriori estimation of MRF disparity labels (SRM) 60 . The global disparity estimation procedures chosen for comparison were: binocular narrow-baseline stereo matching procedure using a max-tree data structure (MTS) 61 and its improvement (MTS-2) 62 , and an accelerated multi-block matching (MBM) algorithm on GPU 22 .…”

Section: Resultsmentioning

confidence: 99%

“…err) of the FGS algorithm vs state-of-the-art non-learning-based disparity estimation algorithms for stereo pairs in the Middlebury evaluation dataset version 3.0. Images (Weight) FGS HCS 49 ACR-GIF-OW 54 MANE 55 SRM 60 MTS 61 DAWA-F 56 PPEP-GF 57 MTS-2 62 ADSR-GIF 58 FASW 63 SM-AWP 59 MBM 22 Adiron (8) 3.63 3.98 4.53 11.60 2.88 19.00 4.37 8.12 21.50 6.40 2.86 10.5 4.39 ArtL (8) 4.81 4.31 8.41 22.90 5.96 22.50 13.00 14.80 22.40 9.00 8.03 19.9 8.80 Jadepl (8) 26.72 27.22 22.10 45.90 24.70 123.00 44.40 46.90 108.00 …”

Section: Resultsmentioning

confidence: 99%

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A novel factor graph-based optimization technique for stereo correspondence estimation

Shabanian

Balasubramanian

2022

Sci Rep

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Dense disparities among multiple views are essential for estimating the 3D architecture of a scene based on the geometrical relationship between the scene and the views or cameras. Scenes with larger extents of homogeneous textures, differing scene illumination among the multiple views and with occluding objects affect the accuracy of the estimated disparities. Markov random fields based methods for disparity estimation address these limitations using spatial dependencies among the observations and among the disparity estimates. These methods, however, are limited by spatially fixed and smaller neighborhood systems or cliques. Recent learning-based methods generate rich set of stereo features for generating cost volume and estimating disparity. In this work, we present a new factor graph-based probabilistic graphical model for disparity estimation that allows a larger and a spatially variable neighborhood structure determined based on the local scene characteristics. Our algorithm improves the accuracy of disparity estimates in stereo image pairs with varying texture and illumination characteristics by enforcing spatial dependencies among scene characteristics as well as among disparity estimates. We evaluated our method using the Middlebury benchmark stereo datasets and the Middlebury evaluation dataset version 3.0 and compared its performance with recent state-of-the-art disparity estimation algorithms. Our factor graph-based algorithm provided disparity estimates with higher accuracy when compared to the recent non-learning- and learning-based disparity estimation algorithms. The factor graph formulation can be used for obtaining maximum a posteriori estimates from models or optimization problems with complex dependency structure among hidden variables. The strategies of using a priori distributions with shorter support and spatial dependencies were useful for reducing the computational cost and improving message convergence in the model. The factor-graph algorithm is also useful for other dense estimation problems such as optical flow estimation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A novel factor graph-based optimization technique for stereo correspondence estimation

Shabanian

Balasubramanian

2022

Sci Rep

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“…Yan et al [17] used Markov random field and Bayesian prediction models to achieve stereo matching. Vázquez-Delgado et al [18] first estimated disparity maps with different window sizes using the sum of absolute differences as a local correlation metric and then using Sobel gradient and Fuzzy Inference System to get the disparity image.…”

Section: Related Workmentioning

confidence: 99%

A binocular reconstruction based on perspective projection constraints and its application on robot eye‐hand coordination

Wei

Ling-jiang

2022

IET Computer Vision

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Stereo matching algorithms have been developed for many years but basically focus only on the implementation of existing datasets and are rarely applied to real scenarios, such as industrial robot scenarios. Traditional stereo matching algorithms have a high error rate, and deep learning algorithms are difficult to obtain good results in real scenarios because of their weak generalisation ability and difficult access to training data. In order to use stereo matching algorithms for industrial robot guidance, it is better to design a new traditional algorithm with low time complexity for the characteristics of industrial robot scenarios dominated by planar facets. This paper proposes a new matching method based on subrows of pixels, instead of individual pixels, in order to improve robustness of matching and reduce running time. First, the pixel strings from the same row of the left and right images are divided into several colour-identical or colour-gradient segments. Then, the colour and length of the two left and right pixel segment are used as clues to determine a matching relation and obtain the matching type. Then, all match types can be determined according to non-crossing mapping. Each match type can reason backward to the corresponding spatial state of the stimulus source so that the disparity of pixels in pixel segments representing the spatial state can be calculated. This new matching method makes full use of the stimulus homology constraints and projective geometric constraints of row-aligned images. The method can obtain good results in industrial robot scenarios and be applied for industrial robot guidance.

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“…A summary of all the assessment metrics and a weighted performance measure for the FGS algorithm is presented in Table 4. [49], real-time stereo matching algorithm with FPGA architecture (MANE) [50],…”

Section: Performance Of the Fgs Algorithm Vs State-of-the-art Algorithmsmentioning

confidence: 99%

A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation

Shabanian¹,

Balasubramanian²

2021

Preprint

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Dense disparities among multiple views is essential for estimating the 3D architecture of a scene based on the geometrical relationship among the scene and the views or cameras. Scenes with larger extents of heterogeneous textures, differing scene illumination among the multiple views and with occluding objects affect the accuracy of the estimated disparities. Markov random fields (MRF) based methods for disparity estimation address these limitations using spatial dependencies among the observations and among the disparity estimates. These methods, however, are limited by spatially fixed and smaller neighborhood systems or cliques. In this work, we present a new factor graph-based probabilistic graphical model for disparity estimation that allows a larger and a spatially variable neighborhood structure determined based on the local scene characteristics. We evaluated our method using the Middlebury benchmark stereo datasets and the Middlebury evaluation dataset version 3.0 and compared its performance with recent state-of-the-art disparity estimation algorithms. The new factor graph-based method provided disparity estimates with higher accuracy when compared to the recent non-learning-and learning-based disparity estimation algorithms. In addition to disparity estimation, our factor graph formulation can be useful for obtaining maximum a posteriori solution to optimization problems with complex and variable dependency structures as well as for other dense estimation problems such as optical flow estimation.

show abstract

Real‐time multi‐window stereo matching algorithm with fuzzy logic

Cited by 9 publications

References 51 publications

A novel factor graph-based optimization technique for stereo correspondence estimation

A novel factor graph-based optimization technique for stereo correspondence estimation

A binocular reconstruction based on perspective projection constraints and its application on robot eye‐hand coordination

A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation

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