关于图像处理中特征点描述算子的一点总结
1.SIFt算子SIFT算子是David
G Lowe在2004提出的，即尺度不变特征变换(Scale
Invariant Feature Transform)。它是以尺度空间的构造为基础的局部特征描述算子，对于图像的缩放、旋转和放射变换等具有不变性。SIFT算子在构建好的尺度空间的基础上搜索尺度空间中的极值点(特征点)，然后确定极值点的尺度信息以及位置，再确定极值点的方向(其邻域梯度的主方向)，最终可以得到具有鲁棒性的128维(4*4*8)的特征描述子。
2.surf特征
SURF mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">（Speed-Up Robust Features style="mso-bidi-font-size:12.0pt;line-height:125%; font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-hansi-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman";mso-font-kerning:0pt; mso-ansi-language:FR">）算子 style="mso-bidi-font-size:12.0pt; line-height:125%;font-family:宋体;mso-ascii-font-family:"Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">选取图像在尺度空间上的极值点作为候选特征点。与SIFT style="mso-bidi-font-size: 12.0pt;line-height:125%;font-family:宋体;mso-ascii-font-family:"Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">算子不同的是SURF mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">算子采用Hessian Roman";mso-bidi-font-family: "Times New Roman";mso-font-kerning:0pt;mso-ansi-language:FR">矩阵行列式近似值来构造金字塔。提取SURF style="mso-bidi-font-size: 12.0pt;line-height:125%;font-family:宋体;mso-ascii-font-family:"Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">特征点需要4 mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">个步骤：提取SURF Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">特征，对于特征点进行定位，赋予主方向，生成特征点描述符。 3. BRIEF特征 BRIEF mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">特征(binary robust independent elementary features) style="mso-bidi-font-size:12.0pt;line-height: 125%;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-hansi-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman";mso-font-kerning:0pt; mso-ansi-language:FR">是Calonder style="mso-bidi-font-size:12.0pt; line-height:125%;font-family:宋体;mso-ascii-font-family:"Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">等 style="mso-bidi-font-size:12.0pt;line-height:125%;font-family:宋体;mso-ascii-font-family: "Times New Roman";mso-hansi-font-family:"Times New Roman";mso-bidi-font-family: "Times New Roman";mso-font-kerning:0pt;mso-ansi-language:FR">在2010 mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">年提出来的，他采用二进制字符串作为特征点描述符，因而在速度和性能上都有着卓越的表现。其主要思路是：在特征点附近随机的选取若干点对，将这些点对的灰度值大小组合成一个长为256 mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">的二进制字串，并将这个二进制字串作为该特征点的特征描述子。由于其描述子利用二进制（“0” mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">和“1” style="mso-bidi-font-size: 12.0pt;line-height:125%;font-family:宋体;mso-ascii-font-family:"Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">）编码，因此在特征匹配时只需计算2 mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; mso-font-kerning:0pt;mso-ansi-language:FR">个特征点描述子的Hamming