Local Stereo Matching with Improved Matching Cost and Disparity Refinement

Jiao, Jianbo; Wang, Ronggang; Wang, Wenmin; Dong, Shengfu; Wang, Zhenyu; Gao, Wen

doi:10.1109/mmul.2014.51

Cited by 66 publications

(30 citation statements)

References 16 publications

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“…In summary, the proposed stereo matching method achieves better disparity estimation quality than the methods proposed in [15,18,21] and [27] in Middlebury datasets, especially in the normal stereo datasets. For non-ideal conditions in light, large disparity, and angle moving stereo datasets, the proposed and the other methods have the same tendency in these datasets.…”

Section: Performance Comparisonsmentioning

confidence: 89%

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Robust stereo matching with trinary cross color census and triple image-based refinements

Chang

Yang

2017

EURASIP J. Adv. Signal Process.

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For future 3D TV broadcasting systems and navigation applications, it is necessary to have accurate stereo matching which could precisely estimate depth map from two distanced cameras. In this paper, we first suggest a trinary cross color (TCC) census transform, which can help to achieve accurate disparity raw matching cost with low computational cost. The two-pass cost aggregation (TPCA) is formed to compute the aggregation cost, then the disparity map can be obtained by a range winner-take-all (RWTA) process and a white hole filling procedure. To further enhance the accuracy performance, a range left-right checking (RLRC) method is proposed to classify the results as correct, mismatched, or occluded pixels. Then, the image-based refinements for the mismatched and occluded pixels are proposed to refine the classified errors. Finally, the image-based cross voting and a median filter are employed to complete the fine depth estimation. Experimental results show that the proposed semi-global stereo matching system achieves considerably accurate disparity maps with reasonable computation cost.

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Section: Performance Comparisonsmentioning

confidence: 89%

“…In this subsection, we compare the proposed system to four related methods, which are census-based semiglobal stereo matching [21], cross-based local stereo matching [15], combined cost-based local stereo matching [27], and belief propagation-based global stereo matching [18]. In Fig.…”

Section: Performance Comparisonsmentioning

confidence: 99%

Robust stereo matching with trinary cross color census and triple image-based refinements

Chang

Yang

2017

EURASIP J. Adv. Signal Process.

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“…For example, when the image radiometric condition is good, the absolute difference (AD), the insensitive measure of Birchfield and Tomasi (BT), or the gradient measure can achieve accurate matching results (Meiet et al, 2011). When the image radiation varies, zero-based normalized cross correlation (ZNCC) and normalized gradient (Zhou and Boulanger, 2012) are often used to compensate for linear radiation distortions between correspondences, while Census (Zabih and Woodfill, 2005;Jiao et al, 2014;Kordelas et al, 2015), mutual information (Paul et al, 1997), and image radiation correction (Jung et al, 2013) are insensitive to nonlinear radiation distortion. Hirschmuller evaluated the popular cost computation methods and concluded that Census and mutual information measures can achieve the best matching results under varying radiometric conditions (Hirschmueller and Scharstein, 2009).…”

Section: Review Of Previous Workmentioning

confidence: 99%

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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“…The -L‖ component of the lab color space closely matches for the easy human perception. There are many image processing algorithms applying the important transformations discussed in [1] [9]; we convert the left and right images from RGB to the Lab color space and retain only the L values of its pixels for further processing. The color space xyz conversion is also used for result comparison with the lab color space.…”

Section: A Preprocessingmentioning

confidence: 99%

Spatiotemporal Data Fusion using Dictionary Learning and Temporal Edge Primitives

Rao¹,

Rao²,

Kumar³

et al. 2015

IJIGSP

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Abstract-The main target of stereo matching algorithms is to find out the three dimensional (3D) distance, or depth of objects from a stereo pair of images. Depth information can be derived from images using disparity map of the same scene. There are many applications of computer vision like People tracking, Gesture recognition, Industrial automation and inspection, Security and Biometrics, Three-dimensional modeling, Web and Cloud, Aerial surveys etc. There are large categories of stereo algorithms which are used for finding the disparity or depth. This paper presents a proposed stereo matching algorithm to obtain depth map, enhance and measure. The hybrid mathematical process of the algorithm are color conversion, block matching, guided filtering, Minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last non linear spatial filtering. Our algorithm is produce noise less, reliable, smooth and efficient depth map. We obtained the results with ground truth image using Structural Similarity Index Map (SSIM) and Peak Signal to Noise Ratio (PSNR).

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Local Stereo Matching with Improved Matching Cost and Disparity Refinement

Cited by 66 publications

References 16 publications

Robust stereo matching with trinary cross color census and triple image-based refinements

Robust stereo matching with trinary cross color census and triple image-based refinements

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

Spatiotemporal Data Fusion using Dictionary Learning and Temporal Edge Primitives

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