Improved MAR Natural Feature Recognition Algorithm Based on SURF and ORB

Abstract

To solve the problem of slow image feature extraction and poor matching accuracy in mobile augmented reality (MAR) scene recognition, this paper proposes an improved natural feature recognition algorithm based on SURF（Speeded Up Robust Features） and ORB（Oriented FAST and Rotated BRIEF）. Firstly, the image is preprocessed, including Gaussian smoothing, grayscale and histogram equalization to reduce the impacts of noise on image feature extraction; the image can be normalized through extracting useful information of image features, and then the image center will be selected as the feature point. Secondly, the SURF and ORB algorithm are respectively used to describe the image feature points to determine the orientation of the image feature points so that the image could have the rotation invariance. Finally, the K-Nearest Neighbors (KNN) algorithm is used to select the SURF space and the ORB spatial neighboring image, respectively, and the image weight, i.e. the weighted KNN algorithm, is given to form a new image set, and the image with the smallest weight is selected as the matching image. Experimental results show that when the feature extraction time and matching time are less than 3 ms on Ordinary laptop, meanwhile the image matching accuracy is as high as 92.5%, the computing speed and matching accuracy are better than traditional algorithms. Therefore, the natural feature recognition of the image can be realized in real time and accurately.