Improved SIFT-features matching for object recognition

The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm's robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains. To evaluate the performance of the proposed approach, it was applied to real images acquired with the stereo camera system of the rehabilitation robotic system FRIEND II. The experimental results show an increase in the number of correct features matched and, at the same time, a decrease in the number of outliers in comparison with the original SIFT algorithm. Compared with the original SIFT algorithm, a 40% reduction in processing time was achieved for the matching of the stereo images.