SIFT has proved to be the most robust local invariant feature descriptor in object recognition and matching.
目前，SIFT已经被证明鲁棒性最好的局部不变特征描述符。
This paper discusses a simple method of SIFT operator, operator is simplified greatly reduces the dimensions of feature point descriptor is compared with the original operator.
本文讨论了一种SIFT算子的简化方法，简化后的算子与原算子相比大大降低了特征点描述子的维度。
Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes.
我们的描述符的灵感来自较早的如SIFT和GLOH，但我们的目的，可以更快的计算。
A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed.
针对遥感图像配准，基于尺度不变特征变换(SIFT)提出了一种在核空间中构建仿射不变描述子的方法。
A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed.
针对遥感图像配准，基于尺度不变特征变换(SIFT)提出了一种在核空间中构建仿射不变描述子的方法。
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