论文标题
部分可观测时空混沌系统的无模型预测
Comparison of Stereo Matching Algorithms for the Development of Disparity Map
论文作者
论文摘要
立体声匹配是计算机愿景中针对3D信息提取的经典问题之一，但仍然引起争议。匹配技术和成本功能的使用对于差异图的发展至关重要。本文介绍了六种不同的立体声匹配算法的比较研究，包括块匹配（BM），与动态编程（BMDP），信念传播（BP），梯度特征匹配（GF），定向梯度（HOG）的直方图（HOG）直方图以及提议的方法。还使用并比较了三个成本函数，即平均平方误差（MSE），绝对差异（SAD）的总和，归一化互相关（NCC）并进行了比较。这项研究中使用的立体声图像来自具有完美且不完美的校准的米德尔伯里立体声数据集。结果表明，匹配函数的选择非常重要，也取决于图像属性。结果表明，在大多数情况下，BP算法提供了更好的结果，使精度超过95％。
Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG), and the proposed method. Also three cost functions namely Mean Squared Error (MSE), Sum of Absolute Differences (SAD), Normalized Cross-Correlation (NCC) were used and compared. The stereo images used in this study were from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. Results show that the selection of matching function is quite important and also depends on the images properties. Results showed that the BP algorithm in most cases provided better results getting accuracies over 95%.
