Stereo matching algorithm for mineral images based on improved BT-Census

IF 4.9 2区工程技术 Q1 ENGINEERING, CHEMICAL

Minerals Engineering Pub Date : 2024-08-11 DOI:10.1016/j.mineng.2024.108905

Lirong YANG, Hui YANG, Yang LIU, Chong CAO

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引用次数: 0

Abstract

Binocular stereo matching is crucial for identifying and locating minerals on the grizzly, allowing the robotic system to carry out crushing autonomously. The traditional stereo matching algorithm yields a low matching rate due to the relatively single color and weak texture of the mineral image caused by the uneven illumination in the field. An improved Birchfield-Tomasi (BT)-Census algorithm is proposed to enhance the capability of discriminating the mineral region and increase the successful matching rate. Firstly, the Gaussian-weighted average grey value of the circular window is used as the central value of the Census transform, and the initial surrogate value is obtained by weighting and fusing the Census cost and the BT cost. Subsequently, the cost aggregation method by adaptive windows is used, and then scanline optimization is applied to select the optimal matching cost. The performance evaluation results using the Middlebury dataset show that the proposed algorithm achieves a 93.33% average successful matching rate, outperforming Absolute Difference of Intensity (AD)-Census, Semi-Global Matching (SGM), and PatchMatch algorithms by 6.5%, 8.04%, and 4.62% respectively. Moreover, In the three-dimensional (3D) reconstruction experiments of minerals on grizzly, the point cloud reconstructed by the proposed method shows significant improvement in terms of accuracy. Notably, in comparison to the SGM algorithm, there is an 83.4% reduction in Mean-Square Error (MSE), a 35.4% reduction in Root Mean-Square Error (RMSE), and a 35.8% reduction in Mean Absolute Error (MAE). Against the AD-Census algorithm, reductions of 47.8% in MSE, 21.6% in RMSE, and 21.4% in MAE are observed. Similarly, in comparison to the PatchMatch algorithm, there are reductions of 11.9% in MSE, 5.8% in RMSE, and 6.1% in MAE. In a word, the proposed improved BT-Census stereo matching algorithm effectively enhances the detailed features of the minerals and improve the successful matching rate and accuracy.

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基于改进型 BT-Census 的矿物图像立体匹配算法

双目立体匹配对于识别和定位灰熊身上的矿物，使机器人系统能够自主进行破碎至关重要。传统的立体匹配算法匹配率较低，原因是野外光照不均导致矿物图像颜色相对单一且纹理较弱。本文提出了一种改进的 Birchfield-Tomasi（BT）-Census 算法，以增强对矿物区域的判别能力，提高匹配成功率。首先，将圆窗的高斯加权平均灰度值作为普查变换的中心值，通过对普查成本和 BT 成本进行加权和融合，得到初始代用值。然后，使用自适应窗口的成本聚合方法，再应用扫描线优化来选择最佳匹配成本。使用 Middlebury 数据集进行的性能评估结果表明，所提算法的平均匹配成功率为 93.33%，分别比绝对强度差（AD）-Census、半全局匹配（SGM）和 PatchMatch 算法高出 6.5%、8.04% 和 4.62%。此外，在灰熊矿物质的三维（3D）重建实验中，拟议方法重建的点云在精确度方面有显著提高。值得注意的是，与 SGM 算法相比，均方误差（MSE）降低了 83.4%，均方根误差（RMSE）降低了 35.4%，平均绝对误差（MAE）降低了 35.8%。与 AD-Census 算法相比，MSE 降低了 47.8%，RMSE 降低了 21.6%，MAE 降低了 21.4%。同样，与 PatchMatch 算法相比，MSE 降低了 11.9%，RMSE 降低了 5.8%，MAE 降低了 6.1%。总之，所提出的改进型 BT-Census 立体匹配算法有效地增强了矿物的细节特征，提高了匹配成功率和准确率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Minerals Engineering 工程技术-工程：化工

CiteScore

8.70

自引率

18.80%

发文量

519

审稿时长

81 days

期刊介绍： The purpose of the journal is to provide for the rapid publication of topical papers featuring the latest developments in the allied fields of mineral processing and extractive metallurgy. Its wide ranging coverage of research and practical (operating) topics includes physical separation methods, such as comminution, flotation concentration and dewatering, chemical methods such as bio-, hydro-, and electro-metallurgy, analytical techniques, process control, simulation and instrumentation, and mineralogical aspects of processing. Environmental issues, particularly those pertaining to sustainable development, will also be strongly covered.

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