scholarly journals Hierarchical Guided-Image-Filtering for Efficient Stereo Matching

2019 ◽  
Vol 9 (15) ◽  
pp. 3122 ◽  
Author(s):  
Chengtao Zhu ◽  
Yau-Zen Chang
Keyword(s):  
Stereo Matching ◽  
Image Filtering ◽  
Data Sets ◽  
Matching Accuracy ◽  
Edge Preserving ◽  
Novel Approach ◽  
Combination Scheme ◽  
Image Pairs ◽  
Guided Image Filtering ◽  
The Cost

Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at different resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.

Local Stereo Matching: An Adaptive Weighted Guided Image Filtering-Based Approach

2020 ◽  
pp. 2154010
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Stereo Matching ◽  
Stringent Response ◽  
Image Filtering ◽  
Absolute Difference ◽  
Map Building ◽  
Disparity Map ◽  
Vision Systems ◽  
Local Algorithms ◽  
Gradient Algorithms ◽  
Guided Image Filtering

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.

scholarly journals Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Lingyin Kong ◽  
Jiangping Zhu ◽  
Sancong Ying
Keyword(s):  
Stereo Matching ◽  
Computing Time ◽  
Image Filtering ◽  
Computation Method ◽  
Aggregation Method ◽  
Additional Time ◽  
Matching Algorithm ◽  
Cost Aggregation ◽  
Matching Cost ◽  
Guided Image Filtering

Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.

scholarly journals Efficient Stereo Matching Based on Pervasive Guided Image Filtering

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Chengtao Zhu ◽  
Yau-Zen Chang
Keyword(s):  
Stereo Matching ◽  
Image Filtering ◽  
Step Function ◽  
Cost Aggregation ◽  
Dense Stereo Matching ◽  
Spatial Differences ◽  
Effective Cost ◽  
Aggregation Scheme ◽  
Image Pixels ◽  
Guided Image Filtering

This paper presents an effective cost aggregation strategy for dense stereo matching. Based on the guided image filtering (GIF), we propose a new aggregation scheme called Pervasive Guided Image Filtering (PGIF) to introduce weightings to the energy function of the filter which allows the whole image pair to be taken into account. The filter parameters of PGIF are calculated as two-dimensional convolution using the bright and spatial differences between the corresponding pixels, which can be incrementally calculated for efficient aggregation. The complexity of the proposed algorithm is O(N), which is linear to the number of image pixels. Furthermore, the algorithm can be further simplified into O(N/4) without significantly sacrificing accuracy if subsampling is applied in the stage of parameter calculation. We also found that a step function to attenuate noise is required in calculating the weights. Experimental evaluation on version 3 of the Middlebury stereo evaluation datasets shows that the proposed method achieves superior disparity accuracy over state-of-the-art aggregation methods with comparable processing speed.

Dense Stereo Matching Based on Cross-Scale Guided Image Filtering

2018 ◽  
Vol 38 (1) ◽  
pp. 0115004
Author(s):  
刘杰 Liu Jie ◽  
张建勋 Zhang Jianxun ◽  
代煜 Dai Yu ◽  
苏赫 Su He
Keyword(s):  
Stereo Matching ◽  
Image Filtering ◽  
Dense Stereo Matching ◽  
Guided Image Filtering

scholarly journals Improving stereo matching algorithm with adaptive cross-scale cost aggregation

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775154 ◽  
Author(s):  
Cong Bai ◽  
Qing Ma ◽  
Pengyi Hao ◽  
Zhi Liu ◽  
Jinglin Zhang
Keyword(s):  
Stereo Matching ◽  
Disparity Estimation ◽  
Data Sets ◽  
Human Beings ◽  
Stereo Correspondence ◽  
Matching Accuracy ◽  
Automatic Driving ◽  
Cost Aggregation ◽  
Correspondence Matching ◽  
Consistency Constraints

Human beings process stereoscopic correspondence across multiple purposes like robot navigation, automatic driving, and virtual or augmented reality. However, this bioinspiration is ignored by state-of-the-art dense stereo correspondence matching methods. Cost aggregation is one of the critical steps in the stereo matching method. In this article, we propose an optimized cross-scale cost aggregation scheme with coarse-to-fine strategy for stereo matching. This scheme implements cross-scale cost aggregation with the smoothness constraint on neighborhood cost, which essentially extends the idea of the inter-scale and intra-scale consistency constraints to increase the matching accuracy. The neighborhood costs are not only used in the intra-scale consistency to ensure that the regularized costs vary smoothly in an eight-connected neighbors region but also incorporated with inter-scale consistency to optimize the disparity estimation. Additionally, the improved method introduces an adaptive scheme in each scale with different aggregation methods. Finally, experimental results evaluated both on classic Middlebury and Middlebury 2014 data sets show that the proposed method performs better than the cross-scale cost aggregation. The whole stereo correspondence algorithm achieves competitive performance in terms of both matching accuracy and computational efficiency. An extensive comparison, including the KITTI benchmark, illustrates the better performance of the proposed method also.

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