PDF(Pubmed)
Abstract：
To address the issues of low measurement accuracy and unstable results when using binocular cameras to detect objects with sparse surface textures, weak surface textures, occluded surfaces, low-contrast surfaces, and surfaces with intense lighting variations, a three-dimensional measurement method based on an improved feature matching algorithm is proposed. Initially, features are extracted from the left and right images obtained by the binocular camera. The extracted feature points serve as seed points, and a one-dimensional search space is established accurately based on the disparity continuity and epipolar constraints. The optimal search range and seed point quantity are obtained using the particle swarm optimization algorithm. The zero-mean normalized cross-correlation coefficient is employed as a similarity measure function for region growing. Subsequently, the left and right images are matched based on the grayscale information of the feature regions, and seed point matching is performed within each matching region. Finally, the obtained matching pairs are used to calculate the three-dimensional information of the target object using the triangulation formula. The proposed algorithm significantly enhances matching accuracy while reducing algorithm complexity. Experimental results on the Middlebury dataset show an average relative error of 0.75% and an average measurement time of 0.82 s. The error matching rate of the proposed image matching algorithm is 2.02%, and the PSNR is 34 dB. The algorithm improves the measurement accuracy for objects with sparse or weak textures, demonstrating robustness against brightness variations and noise interference.
摘要：
为解决使用双目相机检测表面纹理稀疏的物体时测量精度低、结果不稳定的问题,弱表面纹理，遮挡表面,低对比度表面，和强烈的光照变化的表面，提出了一种基于改进特征匹配算法的三维测量方法。最初，从双目相机获得的左右图像中提取特征。提取的特征点作为种子点，基于视差连续性和极线约束，准确地建立了一维搜索空间。利用粒子群优化算法获得最优搜索范围和种子点数量。零均值归一化互相关系系数被用作区域生长的相似性度量函数。随后，根据特征区域的灰度信息对左右图像进行匹配，在每个匹配区域内执行种子点匹配。最后,得到的匹配对用于利用三角剖分公式计算目标物体的三维信息。该算法在降低算法复杂度的同时显著提高了匹配精度。在Middlebury数据集上的实验结果表明,平均相对误差为0.75%,平均测量时间为0.82s。PSNR为34dB。该算法提高了纹理稀疏或弱的物体的测量精度,证明了对亮度变化和噪声干扰的鲁棒性。
