This paper presents a new method for image feature generation called the Scale Invariant Feature Transform(SIFT).This approach transforms an image into a large collection of local feature vectors, each of which is invariant to image translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection. Previous approaches to local feature generation lacked invariance to scale and were more sensitive to projective distortion and illumination change. The SIFT features share a number of properties in common with the responses of neurons in inferior
temporal (IT) cortex in primate vision. This paper also describes improved approaches to indexing and model verification.

本文介绍了一种新的图像特征产生的算法，叫做尺度不变特征变换（SIFT）。这种方法是将一幅图像转换成一个大的局部特征向量的集合，每个向量在图像转换、缩放、轮换、光照变化的部分不变、仿射或三维投影中是不变的。以前产生局部特征的方法缺乏轮换不变性以及对投影变形和光照变化更加敏感。SIFT算法的特征与在颞下灵长类视觉皮层的神经元的响应有着很多相似的性质。本文还介绍了索引和模型验证的改进方法。