Image Feature Matching and Object Detection Using Brute-Force Matchers

Abstract

The paper considers a problem of feature matching and object detection in two images using brute-force matchers. The proposed framework exploited several concurrent algorithms for feature detection and descriptor extraction, such as ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), SIFT (Scale Invariant Feature Transform) and SURF (Speeded-Up Robust Features). The feature matching is accomplished by the Brute-Force approach combined with the k-Nearest Neighbors algorithm. The obtained matches are utilized by the robust RANSAC (Random Sample Consensus) method for estimating the transformation between two consecutive images. Therefore, the RANSAC method is employed to improve the outliers removal. The proposed algorithm is designed and implemented using OpenCV library. Its effectiveness and quality are verified through analyses of its execution speed and accuracy of the feature matching.