Image Stitching Using Smoothly Planar Homography

Xiang, Tian-Zhu; Xia, Gui-Song; Zhang, Liangpei

doi:10.1007/978-3-030-03398-9_45

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…We are rewriting Equation ( 6) using matrix notation as Ah = 0 results in the expression for Equation (7).…”

Section: Homography Principlementioning

confidence: 99%

“…Today, images captured from various viewpoints or equipment, including remote sensing satellites, can display considerable geometric and photometric variances caused by factors like fluctuating environmental conditions, dissimilarities in camera technology, and unstable shooting scenarios. This difference makes combining or comparing these images difficult, affecting many applications such as augmented reality [1][2][3], object recognition [4,5], and panoramic stitching [6][7][8]. To solve this problem, researchers have employed various image processing techniques such as image registration [9,10], color correction [11,12], and homography estimation [13].…”

Section: Introductionmentioning

confidence: 99%

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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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“…We are rewriting Equation ( 6) using matrix notation as Ah = 0 results in the expression for Equation (7).…”

Section: Homography Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Homography Estimation: Advances and Challenges

Luo,

Wang,

Liao

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Moreover, the optimized warping for the homography model [9], [31] effectively solves the distortion problem caused by the change of viewing angle. Moreover, reasonable meshing to adapt to the coplanar characteristics of homography can also achieve better visual effects [32]- [33]. The warps have achieved considerable splicing results, but they are difficult to cope with complex scenes (see Figure 1(e)).…”

Section: Introductionmentioning

confidence: 99%