Abstract
Similarity measures based on correlation have been used extensively for matching tasks. However, traditional correlation-based image matching methods are sensitive to rotation and scale changes. This paper presents a fast correlation-based method for matching two images with large rotation and significant scale changes. Multiscale oriented corner correlation (MOCC) is used to evaluate the degree of similarity between the feature points. The method is rotation invariant and capable of matching image pairs with scale changes up to a factor of 7. Moreover, MOCC is much faster in comparison with the state-of-the-art matching methods. Experimental results on real images show the robustness and effectiveness of the proposed method.
Highlights
Matching two images of the same scene or object is one of the fundamental problems in computer vision
Our work addresses the problem of matching image pairs with large rotation and significant scale changes, which cannot be efficiently solved by traditional correlation-based methods
This paper presents a new method named Multiscale oriented corner correlation (MOCC) (Multiscale Oriented Corner Correlation) for matching two uncalibrated images under large scale changes
Summary
Matching two images of the same scene or object is one of the fundamental problems in computer vision. Image matching plays an important role in many applications such as stereo correspondence, motion analysis, image registration, and image/video retrieval. The methods for image matching can be broadly divided into two classes: area-based matching and feature-based matching. Area-based matching directly compares the gray value distribution in image patches, and the similarity is measured by cross-correlation or least-squares techniques. Feature-based matching extracts salient features such as corners in the two images and establishes reliable feature correspondences by comparing the feature descriptors. There have been some matching methods that can be regarded as the combination of the two classes [4, 5]
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