提出了一种适用于彩色图像的局部不变特征配准方法。特征点提取阶段，提出了快速分割测试特征颜色差异(CDoFAST)特征点检测方法，计算图像的颜色不变量，以此为输入在尺度空间检测FAST 极值点，在极值点附近对高斯差分算子(DoG)值进行插值和拟合，以最终确定特征点的位置和尺度。特征描述符生成阶段，提出了一种新的彩色二进制局部不变特征(CBLID)，采样点邻域结构类似于人眼视觉的重叠，通过统计方向图生成二进制链码，具备旋转、尺度缩放、光照不变性和抗噪性能。通过计算汉明距离进行匹配并结合随机抽样一致性(RANSAC)算法去除误匹配点，计算出待配准图像间的变换关系。实验表明，所提算法针对彩色图像能够获得比传统的尺度不变特征变换(SIFT)、快速稳健特征(SURF)和DAISY 更高的配准精度，同时算法的运行时间也较短，在测试图片上耗时仅为SIFT的10%和12%。

A novel local invariant feature based image registration method for color image is proposed. In the stage of feature point extraction, a new method named colored difference of features from accelerated segment test (CDoFAST) is proposed. The color invariant value of the image is calculated, and FAST extreme points in scale space are searched. The difference of Gaussian (DoG) value around the extreme points are interpolated and fitted to determine the location and scale of the feature points. In the stage of feature vector extraction, a new colored binary local invariant descriptor (CBLID) is proposed. Its sample pattern is similar to the human visual overlap. By generating binary chain code using the statistics of orientation maps, the descriptor is invariant to rotation, scaling, illumination changes and is robust to noise. The feature vectors are matched by calculating their hamming distance and eliminating wrong matches by random sample consensus (RANSAC). Then the transform matrix between the reference image and the registered image is calculated. The experimental results indicate that the proposed method outperforms other classical methods such as scale invariant feature transform (SIFT), speed up robust feature (SURF) and DAISY in registration accuracy and cost time. The cost time of the proposed method in processing the experimental images are only 10% and 12% of that cost in SIFT.