A Novel Iterative Matching Scheme Based on Homography Method for X-Ray Image

Song, Lujie; Zou, Haibo; Ji, Zhenyu; Xie, Xiaoming; Li, Wei

doi:10.1142/s0219519420500384

Cited by 1 publication

(1 citation statement)

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“…In the medical imaging domain [116][117][118], homography estimation applications have expanded, including tumor motion tracking [116], iterative matching of X-ray images [117], and multimodal medical image alignment (e.g., MRI and CT images) [118]. These applications are vital for improving the accuracy of medical diagnosis and treatment.…”

Section: Applications Fieldsmentioning

confidence: 99%

A Review of Homography Estimation: Advances and Challenges

Luo,

Wang,

Liao

et al. 2023

Electronics

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Images captured from different viewpoints or devices have often exhibited significant geometric and photometric differences due to factors such as environmental variations, camera technology differences, and shooting conditions’ instability. To address this problem, homography estimation has attracted much attention as a method to describe the geometric projection relationship between images. Researchers have proposed numerous homography estimation methods for single-source and multimodal images in the past decades. However, the comprehensive review and analysis of homography estimation methods, from feature-based to deep learning-based, is still lacking. Therefore, we provide a comprehensive overview of research advances in homography estimation methods. First, we provide a detailed introduction to homography estimation’s core principles and matrix representations. Then, we review homography estimation methods for single-source and multimodal images, from feature-based to deep learning-based methods. Specifically, we analyze traditional and learning-based methods for feature-based homography estimation methods in detail. For deep learning-based homography estimation methods, we explore supervised, unsupervised, and other methods in-depth. Subsequently, we specifically review several metrics used to evaluate these methods. After that, we analyze the relevant applications of homography estimation and show the broad application prospects of this technique. Finally, we discuss current challenges and future research directions, providing a reference for computer vision researchers and engineers.

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