关键词: 稀疏矩阵, 加速分割测试特征, 特征点提取, 高斯拉普拉斯算子, 感知哈希, 仿射不变性

Abstract: There are still some problems with the classical feature detection algorithms. Traversing each pixel in image will cost much time which can’t satisfy the real time requirement. A method of fast feature detection with image sparse matrix is proposed. Firstly, the Laplacian of Gaussian（LoG） operator is used to extract the image gradient and obtain the edge sparse matrix. Then, the advanced algorithm of Features from Accelerated Segment Test（FAST） is used on the basis of sparse matrix to choose feature point, which solves the problem of time-consuming in feature detection. In addition, in order to correct mismatches that disturb image registration, perception Hash algorithm is utilized to purify the matching point pair. Finally, the homography matrix is optimized and checked by two constraints established with affine invariance. Experiments show that the algorithm improves the speed of feature extraction and the accuracy of image registration.

Key words: sparse matrix, Features from Accelerated Segment Test（FAST）, feature extraction, Laplacian of Gaussian（LoG）, perception Hash, affine invariance