Abstract
We propose an efficient image-matching method for deformable-object image matching using discrimination of deformable objects and geometric similarity clustering between feature-matching pairs. A deformable transformation maintains a particular form in the whole image, despite local and irregular deformations. Therefore, the matching information is statistically analyzed to calculate the possibility of deformable transformations, and the images can be identified using the proposed method. In addition, a method for matching deformable object images is proposed, which clusters matching pairs with similar types of geometric deformations. Discrimination of deformable images showed about 90% accuracy, and the proposed deformable image-matching method showed an average 89% success rate and 91% accuracy with various transformations. Therefore, the proposed method robustly matches images, even with various kinds of deformation that can occur in them.
Highlights
Computer vision lets a machine or computer see and understand objects, just like human vision
This paper introduced a method of matching images with geometric transformation and deformable transformations using the same features
The possibility of the occurrence of a deformable transformation is discriminated using the results from statistical analysis of the matching information and supervised learning
Summary
Computer vision lets a machine or computer see and understand objects, just like human vision. While recent research has focused on recognition based on big data and deep learning (DL) [1], traditional computer vision methods are still widely used in some specific areas. While DL is not yet used in many applications due to the requirement for high computing power and big data, traditional research based on hand-craft techniques, like feature detection and feature matching, are actively used in various applications, such as machine vision, image stitching, object tracking, and augmented reality. The new current challenge is image matching of deformable objects [2]. Deformable matching methods were researched for augmented reality, remote exploration, and image registration for medical images, and it was not until recently that research on deformable-object matching appeared
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