Abstract

Feature description and matching is a fundamental problem for many computer vision applications. However, most existing descriptors only work well on images of a single modality with similar texture. This paper presents a novel basic descriptor unit called a Gixel, which uses an additive scoring method to sample surrounding edge information. Several Gixels in a circular array create a powerful descriptor called the Gixel Array Descriptor (GAD), excelling in multi-modal image matching, especially when one of the images is edge-dominant with little texture. Experiments demonstrate the superiority of GAD on multi-modal matching, while maintaining a performance comparable to several state-of-the-art descriptors on single modality matching.

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