改进的CenSurE特征和基于相加图像梯度的快速描述符

摘要

This paper proposed a new, real-time and robust local feature and descriptor, which can be applied to computer vision field with high demands in real-time.Since CenSurE has extremely efficient computation, it has got wide attention.Due to its linear scales, the filter response signal is very sparse and cannot acquire high repeatability.Therefore,this paper modified the detector using logarithmic scale sampling, and obtained better performance.The new rapid descriptor was based on gradient of the summed image patch, called GSIP.The GSIP descriptor has superior performance.An extensive experimental evaluation was performed to show that the GSIP descriptor increases the distinctiveness of local image descriptors for image region matching and object recognition compared with the state-of-the-art SURF descriptor.Furthermore, compared with SURF, GSIP achieves a two-fold speed increase.%CenSurE局部特征计算效率非常高,但是CenSurE特征的尺度采样是线性的,滤波器响应信号很稀疏,检测的特征重复率不高.采用对数尺度采样得到改进的CenSurE特征,获得了更高的检测性能.同时,提出基于相加图像梯度的快速描述符,称为GSIP.图像区域匹配和物体识别评价实验结果显示,和目前性能最优的SURF描述符相比,GSIP描述符独特性更强,速度更快,计算时间不到SURF描述符的1/2.