基于主成分分析方向深度梯度直方图的立体视觉深度图特征提取

针对立体视觉深度图特征提取精确度低、复杂度高的问题,提出了一种基于主成分分析方向深度梯度直方图(PCA-HODG)的特征提取算法。首先,对双目立体视觉图像进行视差计算和深度图提取,获取高质量深度图;然后,基于预设大小窗口对所获取的深度图进行边缘检测和梯度计算,获得区域形状直方图特征并量化;同时运用主成分分析(PCA)进行降维;最后,为实现特征获取的精确性和完整性,采用滑动窗口检测方法实现整幅深度图的特征提取,并再次降维。在特征匹配分类实验中,对于Street测试序列帧,该算法比距离样本深度特征(RSDF)算法平均分类准确率提高了1.15%,而对于Tanks、Tunnel、Temple测试序列帧,该算法比测度不变特征(GIF)算法平均分类准确率分别提高了0.69%、1.95%、0.49%;同时与方向深度直方图(HOD)、RSDF、GIF算法相比,平均运行时间分别降低了71.65%、78.05%、80.06%。实验结果表明,该算法不仅能够更精确地检测和提取深度图特征,而且通过降低维数复杂度大大减少了运行时间;同时算法具有较好的鲁棒性。

Feature extraction for stereoscopic vision depth map based on principal component analysis and histogram of oriented depth gradient

To solve the low accuracy and high complexity in feature extraction of stereoscopic vision depth map, a feature extraction algorithm based on Principal Component Analysis and Histogram of Oriented Depth Gradient (PCA-HODG) was proposed. Firstly, disparity computation and depth map extraction were executed for binocular stereoscopic vision image to obtain high quality depth map; secondly, edge detection and gradient calculation of depth map within fixed size window were performed, then the features of region shape histograms were acquired and quantified. Meanwhile, dimensionality reduction by Principal Component Analysis (PCA) was implemented; finally, to realize the accuracy and completeness of feature extraction from depth map, a detection method of sliding window was used for the feature extraction of whole depth map and the dimensionality reduction was implemented once again. In the experiment of feature matching and classification, for the frames of test sequence Street, the average classification accuracy rate of the proposed algorithm increased by 1.15% when compared with the Range-Sample Depth Feature (RSDF) algorithm; while for Tanks, Tunnel, Temple, the average classification accuracy rate increased by 0.69%, 1.95%, 0.49% respectively when compared with the Geodesic Invariant Feature (GIF) algorithm. At the same time, the average running time decreased by 71.65%, 78.05%, 80.06% respectively compared with the Histogram of Oriented Depth (HOD), RSDF, GIF algorithm. The experimental results show that the proposed algorithm can not only detect and extract features of depth map more accurately, but also reduce the running time greatly by dimensionality reduction. Moreover, the proposed algorithm also has better robustness.