Deep Homography Estimation based on Attention Mechanism

Wang, Shuang; Yuan, Feiyun; Chen, Bo; Jiang, Haifei; Chen, Wangqiao; Wang, Yi

doi:10.1109/icsai53574.2021.9664027

Cited by 1 publication

(1 citation statement)

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“…Specifically, introducing content-aware masks has guided the design of future deep homography estimation networks. Numerous scholars have begun to realize that image masks can improve the performance of networks effectively and have begun to incorporate various types of attentional mechanisms into networks for robust homography estimation [82][83][84].…”

Section: Has Performance Advantagesmentioning

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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Section: Has Performance Advantagesmentioning

confidence: 99%