基于图像颜色信息的C-FAST特征检测和匹配算法

C-FAST Feature Detection and Matching Algorithm Based on Image Color Information

以效率较高的加速分割测试特征提取(FAST)算法为基础,添加原FAST算法不具备的尺度不变性和旋转不变性特征描述子,在特征检测和匹配时将颜色信息作为重要参考变量,提出了一种基于颜色信息改进FAST算法的图像特征检测和匹配算法(C-FAST)。改进后的算法效率较高,具有更高的检测和匹配精度,且在光照变化和噪声下均有很好的稳健性。使用公开数据集和常用图像对FAST算法、快速稳健特征(SURF)算法、基于颜色信息的尺度不变特征转换(CSIFT)算法及所提C-FAST算法进行了性能分析。结果表明,所提算法能有效可靠地完成图像的特征检测和匹配,对比原FAST算法,准确率提升30%。

Based on the efficient FAST (features from accelerated segment test) algorithm, a color based-FAST (C-FAST) algorithm for image feature detection and matching with color information improvement is proposed. The algorithm adds scale invariant and rotation invariant feature descriptors which the original FAST algorithm does not have, and takes color information as an important reference factor in feature detection and matching. Hence, the proposed algorithm is more efficient and has higher detection and matching accuracies. It also has good robustness under the conditions of illumination changes and noise effects. Different algorithms like FAST, speeded up robust features (SURF), colorful scale-invariant feature transform (CIFT) and the proposed algorithm are analyzed via public data sets and common images. The running data prove that the proposed algorithm can detect and match the image features effectively and reliably, with 30% accuracy improvement compared with the original FAST algorithm.