Object recognition in cluttered real-world scenes requires local image features that are unaffected by nearby clutter or partial occlusion. The features must be at least partially invariant to illumination, 3D projective transforms, and common object variations. On the other hand, the features must also be sufficiently distinctive to identify specific objects
among many alternatives. The difficulty of the object recognition problem is due in large part to the lack of success in finding such image features. However, recent research on the use of dense local features (e.g., Schmid & Mohr [19]) has shown that efficient recognition can often be achieved by using local image descriptors sampled at a large number of repeatable locations.

物体识别在混乱的真实世界场景中要求局部图像特征在杂波或部分阻塞的附近是自然的。这些特征必须至少是部分光照不变、三维投影变换和公共对象变化的。另一方面，这些特征还必须具有足够的特色来在大量的选择元中确定具体的对象。物体识别问题的难点主要是缺乏找到这种图像特征的成功性。然而，目前关于使用密集局部特征的研究(e.g., Schmid & Mohr [19])表明有效的识别通常能够通过在大量重复的位置进行形象的采样描绘来实现。