SVCV: segmentation volume combined with cost volume for stereo matching

Zhu, Hongmei; Yin, Jingwei; Yuan, Di

doi:10.1049/iet-cvi.2016.0446

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…For cost computation, researchers have proposed numerous cost functions to detect the correlation between correspondence points, including the squared intensity difference (SD), absolute intensity difference (AD), sum of absolute difference (SAD) [11], sum of squared differences (SSD) [4], normalized correlation coefficient (NCC), census transform (CT) [4,9], gradient amplitude (GA) [17,18], and mutual information (MI) [8]. In recent years, neural networks [11] have been increasingly applied to compute matching cost.…”

Section: Cost Computation Based On Multiple Measurementmentioning

confidence: 99%

An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

Yang

Lei

Liu

et al. 2021

IET Image Processing

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Rapidly obtaining accurate dense disparity maps has been the focus of stereo matching research. At present, approaches that achieve superior disparity maps require a large amount of computation, which is not suitable for practical applications. To address this issue, this paper proposes an efficient local matching method based on an adaptive exponentially weighted moving average filter and simple linear iterative clustering segmentation algorithm. First, an effective matching cost is introduced to adaptively integrate absolute intensity difference with Census transform, which is robust against texture free and luminance variate. Following this, during the cost aggregation, the exponentially weighted moving average filter and the SLIC segmentation are combined to handle the problems of computing consumption and adaptive expansion of the cost aggregation window. Finally, the dense disparity map is obtained by a winner-takes-all approach and disparity refinement. To demonstrate its efficiency and validity, the method is quantitatively tested and compared to existing approaches on the Middlebury benchmark. The results show that it has a non-occlusion accuracy of 90.66% and an average runtime of 7.01 s on the 2014 Middlebury dataset. Compared with existing competitive methods, the proposed method achieves superior matching results with a lower time cost. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Cost Computation Based On Multiple Measurementmentioning

confidence: 99%

An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

Yang

Lei

Liu

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…However, it is a local method and time-consuming. And the segmentation information, namely the superpixel structure can be integrated in some methods [15]. PMSC [16] utilizes SLIC [13] to construct a multilayer superpixel structure, and then generates a series of label proposals, and iteratively updates current optimal labels by performing 𝛼-expansion with each proposals.…”

Section: Related Workmentioning

confidence: 99%

LESC: Superpixel cut‐based local expansion for accurate stereo matching

Cheng

Zhao

Yang

et al. 2021

IET Image Processing

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The rapid estimation of the accurate disparity between pixels is the goal of stereo matching. However, it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for high-resolution images. In this paper, a novel superpixel cut-based method is proposed, in an attempt to get the accurate disparity map efficiently, including the multi-layer superpixel optimization and iteractive local 𝛼-expansion in parallel. As for the multi-layer superpixel optimization, feature point optimization is designed to get accurate candidate labels that are set for most pixels using non-local cost aggregation strategy and update per-pixel labels of the corresponding superpixels from the candidate label sets on the small-size superpixel layer, and then update the middle to large-size superpixel layers progressively using non-local cost aggregation strategy. In order to provide more prior information to identify weak texture and textureless regions in non-local cost aggregation, the weight combination of "intensity + gradient + binary image" is proposed for constructing an optimal minimum spanning tree (MST) to calculate the aggregated matching cost and obtain the labels of minimum aggregated matching cost. Moreover, the local patch surrounding the corresponding superpixels is designed to accelerate superpixel optimization in parallel, and a neighborhood structure is presented to optimize the algorithm in this study, including superpixel neighborhood and patch neighborhood. As for the iteractive local 𝛼-expansion, three layers of patch structure corresponding to the superpixel neighborhood structure is proposed for optimizing the algorithm in this study. The experimental results show that higher accuracy could be achieved via the method in this study compared with some known state-of-the-art stereo methods on KITTI 2015 and Middlebury benchmark V3, which are the standard benchmarks for testing the stereo matching methods.

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“…Once the cost of the paired pixels in the stereo images is calculated, the cost aggregation is further applied to achieve more robust results by including more pixels, which have the same tendency. For local stereo matching, the window-based aggregation considers the similarities of the surrounding pixels in a designated window [19][20][21][22][23][24][25]. The ideal windows are designed to include the nearby pixels, which are in the same object as possible.…”

Section: Cost Aggregationmentioning

confidence: 99%

Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Huang

Chan

et al. 2020

J Image Video Proc.

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Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded, and discontinuous regions. In this paper, the proposed stereo matching system uses segment-based superpixels and matching cost. After determination of edge and smooth regions and selection of matching cost, we suggest the segment-based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements use the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for leftmost and rightmost regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system can achieve higher accurate depth maps than the conventional methods.

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SVCV: segmentation volume combined with cost volume for stereo matching

Cited by 9 publications

References 28 publications

An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

LESC: Superpixel cut‐based local expansion for accurate stereo matching

Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

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