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Robust Image Matching for Information Systems Using Randomly Uniform Distributed SURF Features
Abstract
Detection of similar images taken in different perspectives is a big concern in digital image processing. Fast and robust methods have been proposed in this area. In this chapter, a novel image matching approach is proposed by using speeded-up robust features (SURF). SURF is a local feature detector and descriptor that can be used for tasks such as object recognition or registration or classification or 3D reconstruction. Successful detection of the images is achieved by finding and matching corresponding interest points using SURF features. The task of finding correspondences between two images is performed through using a novel brute-force method which uniformly generates random pairs for matching similarity. Experimental results show that the proposed method yields better results than conventional brute force methods in which at least 5% accuracy increment is obtained.
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