针对场景匹配技术中以二进制稳健基元独立特征( binary robust independent elementary features,BRIEF)为代表的实时算法匹配错误率高的问题,提出一种基于局部梯度二值化的特征描述算法.该算法利用重心向量方向归一化特征描述区域,保证了特征描述符的方向不变性.同时,融合基于局部梯度二值化的区域纹理信息以降低特征匹配错误率.使用国际通用数据库对算法进行了验证,实验结果表明：提出的场景匹配算法其平均匹配准确率比BRIEF算法高44.59%,具有较高的鲁棒性.%For real-time scene feature extraction and matching, conventional binary descriptors improve the speed of the descriptor generation and matching procedure while the false matching rate is high, such as Binary Robust Independent Elementary Features ( BRIEF ) which is only based on pixel intensity comparisons. To solve this problem, an improved binary descriptor was proposed in this paper, which preserved not only the pixel intensity information, but also the local texture information based on the gradient value. Additionally, the orientation of the centroid vector was also used in the descriptor calculation process, so that the binary descriptors were orientation-invariant. Image Sequences dataset was used to evaluate the performance of the proposed method, and the average matching accuracy rate of the proposed method was 44. 59%, higher than that of the BRIEF algorithm. Experimental results show that the proposed descriptors have high accuracy and robustness when dealing with image rotation and scale transformation.
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