Improving the cost-volume based local stereo matching algorithm

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

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A New Basic Correlation Measurement for Stereo Matching

10.31219/osf.io/jqux2 ◽

2019 ◽

Author(s):

Bowen Shi ◽

Shan Shi ◽

Junhua Wu ◽

Musheng Chen

Keyword(s):

Time Complexity ◽

Stereo Matching ◽

Computation Method ◽

Local Correlation ◽

Matching Algorithm ◽

Cost Aggregation ◽

Image Patches ◽

Disparity Maps ◽

Aggregation Step ◽

The Cost

In this paper, we propose a new stereo matching algorithm to measure the correlation between two rectified image patches. The difficulty near objects' boundaries and textureless areas is a widely discussed issue in local correlation-based algorithms and most approaches focus on the cost aggregation step to solve the problem. We analyze the inherent limitations of sum of absolute differences (SAD) and sum of squared differences (SSD), then propose a new difference computation method to restrain the noise near objects' boundaries and enlarge the intensity variations in textureless areas. The proposed algorithm can effectively deal with the problems and generate more accurate disparity maps than SAD and SSD without time complexity increasing. Furthermore, proved by experiments, the algorithm can also be applied in some SAD-based and SSD-based algorithms to achieve better results than the original.

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Local stereo matching algorithm using modified dynamic cost computation

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v22.i3.pp1312-1319 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1312

Author(s):

A. F. Kadmin ◽

R. A. Hamzah ◽

M. N. Abd Manap ◽

M. S. Hamid ◽

T. F. Tg. Wook

Keyword(s):

Stereo Matching ◽

Depth Information ◽

Computation Method ◽

Local Method ◽

Matching Algorithm ◽

Depth Measurement ◽

Cost Aggregation ◽

Matching Cost ◽

Winner Takes All ◽

The Cost

Stereo matching is an essential subject in stereo vision architecture. Traditional framework composition consists of several constraints in stereo correspondences such as illumination variations in images and inadequate or non-uniform light due to uncontrollable environments. This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. This approach utilised modified dynamic cost computation in the matching cost. A modified census transform with dynamic histogram is used to provide the cost in the cost computation. The algorithm applied the fixed-window strategy with bilateral filtering to retain image depth information and edge in the cost aggregation stage. A winner takes all (WTA) optimisation and left-right check with adaptive bilateral median filtering are employed for disparity refinement. Based on the Middlebury benchmark dataset, the algorithm developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.

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A Real-Time Infrared Stereo Matching Algorithm for RGB-D Cameras’ Indoor 3D Perception

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9080472 ◽

2020 ◽

Vol 9 (8) ◽

pp. 472

Author(s):

Jiageng Zhong ◽

Ming Li ◽

Xuan Liao ◽

Jiangying Qin

Keyword(s):

Mutual Information ◽

Stereo Matching ◽

Low Cost ◽

Depth Map ◽

Cost Calculation ◽

3D Perception ◽

Detection Range ◽

Matching Algorithm ◽

Map Resolution ◽

The Cost

Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper.

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Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform

Mathematical Problems in Engineering ◽

10.1155/2021/1857327 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Chen Lv ◽

Jiahan Li ◽

Qiqi Kou ◽

Huandong Zhuang ◽

Shoufeng Tang

Keyword(s):

Confidence Interval ◽

Stereo Matching ◽

Color Space ◽

Absolute Difference ◽

Cost Calculation ◽

Matching Accuracy ◽

Matching Algorithm ◽

Hsv Color Space ◽

Census Transform ◽

The Cost

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.

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Towards an Embedded Stereo Matching Algorithm Based on Multiple Correlation Windows

Proceedings of the 13th International Conference on Distributed Smart Cameras ◽

10.1145/3349801.3357128 ◽

2019 ◽

Author(s):

M. A. Palacios-Ramos ◽

H. D. Vázquez-Delgado ◽

Abiel Aguilar-González ◽

M. Pérez-Patricio

Keyword(s):

Stereo Matching ◽

Multiple Correlation ◽

Matching Algorithm

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Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

Remote Sensing ◽

10.3390/rs13020274 ◽

2021 ◽

Vol 13 (2) ◽

pp. 274

Author(s):

Guobiao Yao ◽

Alper Yilmaz ◽

Li Zhang ◽

Fei Meng ◽

Haibin Ai ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Stereo Matching ◽

Least Square ◽

Affine Invariant ◽

Stereo Images ◽

Distance Ratio ◽

Matching Algorithm ◽

End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

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