Real-time local stereo via edge-aware disparity propagation

Sun, Xingming; Mei, Xing; Jiao, Shaohui; Zhou, Min; Liu, Zhihua; Wang, Haitao

doi:10.1016/j.patrec.2014.07.010

Cited by 29 publications

(28 citation statements)

References 18 publications

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“…Large windows take more time for cost aggregation while small windows perform badly in textureless areas. In order to avoid the window definition, the non-local methods, which are based on recursion, were proposed (Yang, 2015;Pham and Jeon, 2013;Cigla and Alantan, 2013;Sun et al, 2014;Cheng et al, 2015), which differed from the local methods in that the cost aggregation of every pixel is supported by the remaining pixels in the whole image for non-local methods. The supports from the remaining pixels depend on the intensity similarity and the cost aggregation path.…”

Section: Review Of Previous Workmentioning

confidence: 99%

“…In recent years, several image-guided non-local methods were proposed, of which the basic mathematic models are consistent essentially (Yang, 2015;Pham and Jeon, 2013;Cigla and Alantan, 2013;Sun et al, 2014;Cheng et al, 2015):…”

Section: Image-guided Non-local Matchingmentioning

confidence: 99%

“…In fact, it is impossible that the intensities of pixels in the same region are exactly the same. In order to strengthen the cost propagation in homogenous intensity regions, traditional methods choose a larger  (  =15~25) to acquire a larger T (Yang, 2015;Pham and Jeon, 2013;Cigla and Alantan, 2013;Sun et al, 2014;Cheng et al, 2015). However, when the cost is propagated between two different regions, T may remain large with a large  , which may bring mismatches in the depth discontinuities.…”

Section: Image-guided Non-local Matchingmentioning

confidence: 99%

“…A good cost aggregation path should be connected. Most non-local methods define paths through horizontal/vertical scanlines (Pham and Jeon, 2013;Cigla and Alantan, 2013;Sun et al, 2014;Cheng et al, 2015). However, the path, which is described by horizontal/vertical scanlines, may be unconnected even though the beginning and the end belong to the same region, as shown in Figure 3.…”

Section: Image-guided Non-local Matchingmentioning

confidence: 99%

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Image-Guided Non-Local Dense Matching With Three-Steps Optimization

Xu¹,

Zhang²,

Yue³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper introduces a new image-guided non-local dense matching algorithm that focuses on how to solve the following problems: 1) mitigating the influence of vertical parallax to the cost computation in stereo pairs; 2) guaranteeing the performance of dense matching in homogeneous intensity regions with significant disparity changes; 3) limiting the inaccurate cost propagated from depth discontinuity regions; 4) guaranteeing that the path between two pixels in the same region is connected; and 5) defining the cost propagation function between the reliable pixel and the unreliable pixel during disparity interpolation. This paper combines the Census histogram and an improved histogram of oriented gradient (HOG) feature together as the cost metrics, which are then aggregated based on a new iterative non-local matching method and the semi-global matching method. Finally, new rules of cost propagation between the valid pixels and the invalid pixels are defined to improve the disparity interpolation results. The results of our experiments using the benchmarks and the Toronto aerial images from the International Society for Photogrammetry and Remote Sensing (ISPRS) show that the proposed new method can outperform most of the current state-of-the-art stereo dense matching methods.

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Section: Review Of Previous Workmentioning

confidence: 99%

Section: Image-guided Non-local Matchingmentioning

confidence: 99%

Section: Image-guided Non-local Matchingmentioning

confidence: 99%

Section: Image-guided Non-local Matchingmentioning

confidence: 99%

See 2 more Smart Citations

Image-Guided Non-Local Dense Matching With Three-Steps Optimization

Xu¹,

Zhang²,

Yue³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Ambrosch and Kubinger [17] implemented a modified version of the Census Transform that expanded the Census Transform to be processed over the intensity image and the absolute value of the gradient in x and y direction. In [18], a two step method was proposed where an initial disparity was computed and only reliable disparity values were aggregated to the cost function for the second step. Jin et al [19] proposed a cost aggregation method that computed ground control points (GCP) in order to refine disparity values using the GCPs, while [20] adapted the support area using assumptions of similarity and proximity values.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Approach for Stereo Matching Suited for Real-Time Processing

Pérez-Patricio

Aguilar-González²,

Camas-Anzueto

et al. 2015

IJCA

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In this paper, a novel method that uses both area and feature based information as similarity measures for stereo matching is proposed. Area-based information is suited for non-homogeneous regions while feature information helps in homogeneous areas. In order to define a conjugate pair, a fuzzy logic approach that combines the similarity information is used. The proposed method preserves discontinuities while reducing matching errors in homogeneous regions. This proposal is suited for real-time processing using dedicated hardware. We demonstrate and discuss performance using synthetic stereo pairs.

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