Separate of multi-objects in image recognition by local features

A multi-objects recognition algorithms which achieve rapid separation of all objects of the match points is proposed. Using SUSAN formed SIFT and ladder-image pyramid structure to achieve the same scale, new algorithm establish a unified set of over-determined equations of all match points. The over-determined linear equations coefficient matrix can be simplified to drop dimension, so simplify the structure of the augmented matrix and whose dimension is reduced to half. The inliers of all objects can be refined according to image transformation properties, and the inliers ensure robustness of the least-squares solution. The results shown that the multi-objects recognition algorithms the least squares iterative is convergence quickly, and the one step iteration will be able to get high accuracy orientation parameters. To discriminate inliers based on SIFT standard 128-dimensional local features, the number of match points is more than doubled SIFT algorithm. The multi-objects separated algorithms of augmented matrix can clearly separate multi-objects and is 2-3 times faster than Hough transform.