Sift算法与VC代码
Sift是David Lowe于1999年提出的局部特征描述子，并于2004年进行了更深入的发展和完善。Sift特征匹配算法可以处理两幅图像之间发生平移、旋转、仿射变换情况下的匹配问题，具有很强的匹配能力。在Mikolajczyk对包括Sift算子在内的十种局部描述子所做的不变性对比实验中，Sift及其扩展算法已被证实在同类描述子中具有最强的健壮性。
总体来说，Sift算子具有以下特性：
（1）Sift特征是图像的局部特征，对平移、旋转、尺度缩放、亮度变化、遮挡和噪声等具有良好的不变性，对视觉变化、仿射变换也保持一定程度的稳定性。
（2）独特性好，信息量丰富，适用于在海量特征数据库中进行快速、准确的匹配。
（3）多量性，即使少数的几个物体也可以产生大量Sift特征向量。
（4）速度相对较快，经优化的Sift匹配算法甚至可以达到实时的要求。
（5）可扩展性强，可以很方便的与其他形式的特征向量进行联合。
Sift特征匹配算法主要包括两个阶段，一个是Sift特征的生成，即从多幅图像中提取对尺度缩放、旋转、亮度变化无关的特征向量；第二阶段是Sift特征向量的匹配。
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