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杨春雨,宋子儒,张 鑫. 基于阈值和权重Census变换的煤矿井下图像立体匹配算法[J]. 煤炭科学技术,2024,52(6):216−225. doi: 10.12438/cst.2023-1169
引用本文: 杨春雨,宋子儒,张 鑫. 基于阈值和权重Census变换的煤矿井下图像立体匹配算法[J]. 煤炭科学技术,2024,52(6):216−225. doi: 10.12438/cst.2023-1169
YANG Chunyu,SONG Ziru,ZHANG Xin. A stereo matching algorithm for coal mine underground images based on threshold and weight under Census transform[J]. Coal Science and Technology,2024,52(6):216−225. doi: 10.12438/cst.2023-1169
Citation: YANG Chunyu,SONG Ziru,ZHANG Xin. A stereo matching algorithm for coal mine underground images based on threshold and weight under Census transform[J]. Coal Science and Technology,2024,52(6):216−225. doi: 10.12438/cst.2023-1169

基于阈值和权重Census变换的煤矿井下图像立体匹配算法

A stereo matching algorithm for coal mine underground images based on threshold and weight under Census transform

  • 摘要: 双目图像立体匹配是实现煤矿井下无人辅助运输车自主避障和视觉侦察的关键技术,但煤矿井下高粉尘、光照条件不稳定等因素会导致视觉传感器采集到的图像产生椒盐噪声,使得立体匹配的误匹配率很高。为此,提出一种基于阈值和权重相结合Census立体匹配算法,减少椒盐噪声对立体匹配影响。具体改进包括:①先对支持窗口内所有像素的灰度值进行阈值处理,去除支持窗口内灰度值极大和极小的像素点,解决异常值对加权融合的影响;②再将中心点对应的4条斜对角像素进行加权融合代替中心点像素,选择中心像素点4条斜对角线上的像素点,步长取1~3,根据对应的步长分别赋予0.7、0.2、0.1的权重,将这12个像素点中有效的像素点分别乘上对应权重,然后除以有效权重之和,得到经过加权处理后的中心像素点值,解决传统算法对Census变换窗口中心点依赖的问题,从而提高匹配精度。试验结果表明:基于阈值和权重Census代价计算全部区域的误匹配率相比传统Census算法降低了5.64%;相比基于均值Census算法降低了1.71%,且在不同噪声下全部区域的误匹配率相比传统Census算法降低了15.93%;相比基于均值Census算法降低了16.62%;而在非遮挡区域,算法的误匹配率相对于传统Census算法降低17.19%,相对于基于均值Census算法降低18.11%。所提出的基于阈值和权重相结合Census立体匹配算法有效的增强了抗噪声的鲁棒性,降低了误匹配率,提高了匹配精度。

     

    Abstract: Binocular image stereo matching is a key technology to realize autonomous obstacle avoidance and visual reconnaissance of unmanned auxiliary transport vehicles in coal mines. However, factors such as high dust and unstable lighting conditions in coal mines can lead to Salt-and-pepper noise in the images collected by the visual sensor, resulting in a high stereo matching error rate. Therefore, a Census stereo matching algorithm based on the combination of threshold and weight is proposed to reduce the impact of Salt-and-pepper noise on stereo matching. The main contributions include: ① threshold processing is carried out on the gray values of all pixels in the support window to remove the pixels with maximum and minimum gray values in the support window and solve the impact of outlier on the weighted fusion; ② the four diagonal pixels corresponding to the center point are weighted and fused to replace the center point pixel. Select pixel points along the four diagonal lines intersecting at the center pixel, with step sizes ranging from 1 to 3. According to the corresponding steps, weights of 0.7, 0.2, and 0.1 are assigned. Multiply the valid pixel points among these 12 points by their respective weights, then divide by the sum of the valid weights. This process yields the reference value of the center pixel point after weighted processing, addressing the issue of traditional algorithms' dependency on the center pixel of the Census transform window. Consequently, this approach enhances matching precision. The experimental results show that the average error rate calculated by the proposed algorithm is reduced by 5.64% compared to traditional Census algorithms, and reduced by 1.71% compared to the mean-based Census algorithm. What's more, the average error rate under different noise levels calculated by the proposed algorithm is reduced by 15.93% compared to the traditional Census algorithm, and reduced by 16.62% compared to the mean-based one. In non-occluded areas, the error matching rate of our algorithm is reduced by 17.19% compared to the traditional Census algorithm and 18.11% compared to the mean-based Census algorithm. The proposed Census stereo matching algorithm, which combines threshold and weight, effectively enhances the robustness against noise, reduces the error rate, and improves matching accuracy.

     

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