Object Recognition Based on Modified Intuitive Corner Detection and Two-stage CornerMatching

This paper proposes an object recognition algorithm based on modified intuitive corner detection and two-stage corner matching. The object recognition algorithm consists of two phases: the off-line training phase and the on-line operating phase. The critical purpose is to construct template database in the training phase. Firstly, the corners are extracted from the template image by the modified intuitive corner detection. The multi-resolution patches are then applied to create the full scale corners’ features. Each corner has its own descriptor based on SIFT and PCA. With this information, the algorithm creates the hierarchical structures of multi-resolution patches to improve the speed of corner matching. In the operating phase, the test images are processed in the same manner mentioned above with single resolution patches, and then the corner will be matched with the multi-resolution patches in the training phase’s database. The two-stage corner matching, coarse and fine matching based on hierarchical structures of corner descriptions appears to reduce the range of patch’s candidates, is then adopted toimprove the matching performance. Finally, the Random sample consensus (RANSAC) criterion is applied to reject the remaining outlier. Experimental results show that our proposed object recognition is reliable and real-time.