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Successful image registration is an important step for object recognition, target detection, remote sensing, multimodal content fusion, scene blending, and disaster assessment and management. The geometric and photometric variations between images adversely affect the ability for an algorithm to estimate the transformation parameters that relate the two images. Local deformations, lighting conditions,...
Successful content-based image registration relies on the accurate identification of corresponding features across images. Geometric and photometric transformations between images may hinder an algorithm's ability to precisely match features. In this work, we propose a novel region descriptor detection and matching algorithm for use with image registration. The detection process utilizes invariant...
Identification of invariant image descriptors is an integral task for many computer vision applications such as image registration, object recognition, and object tracking. The detected features should be invariant to geometric transformations such as rotation and translation, as well photometric variations due to differing lighting conditions. In this work, we propose a unique and effective region...
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