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中文摘要：
针对经典算法存在着运算时间过长或者匹配正确率不高的情况,提出一种扩展的SURF描述符。在原始SURF描述符的基础上,通过计算特征点相应尺度上的邻域采样点的局部归一化灰度统计信息以及二阶梯度值细节信息,形成新的扩展描述符。该方法不但能传承SURF算法速度快的优良性能,还能充分利用图像的灰度信息和细节信息。通过试验表明,综合考虑算法运行效率与匹配正确率,本文算法较原有经典算法更具稳健性。
英文摘要：
To solve the classical methods＇ problems of long executing time or low accuracy, an extended SURF descriptor is proposed. ;n the method using SURF, the proposed method uses local normalization information and second-order gradient values of neighborhood regions to build a new one. Not only does this method perform as fast as SURF algorithm, but it also fully employs the image grayscale information and details. Considering the executing time and rate, experimental results presented in this paper show that the proposed method is more robust than classical methods.
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