Robust Homography Estimation from Local Affine Maps

Rodríguez, Mariano; Facciolo, Gabriele; Morel, Jean‐Michel

doi:10.5201/ipol.2023.356

Cited by 2 publications

(1 citation statement)

References 75 publications

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“…Examples include FT-RANSAC [41], MAGSAC++ [42]. In 2023, Rodríguez et al [43] proposed several modifications to the RANSAC algorithm. The method combines affine approximation and an inverse approach to improve the homography estimation between pairs of images.…”

Section: Feature-based Methodsmentioning

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: Feature-based Methodsmentioning

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

Hybrid model of alternating least squares and root polynomial technique for color correction

et al. 2023

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Colour correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Various researchers have already proposed many approaches. However, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein Alternate Least Square (ALS), and Root Polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to make it look realistic. The proposed model used the Amsterdam Library of Object Images (ALOI) to achieve this objective, which contains a picture of single objects captured under various illumination angles and colors. After this, a hybrid ALS+RP color correction technique is implemented on the dataset image that fixes its color for the reference image. The target image is then converted into three color models i.e., LAB, LUV, and RGB, into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, Median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB to prove its supremacy over other models.

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Robust Homography Estimation from Local Affine Maps

Cited by 2 publications

References 75 publications

A Review of Homography Estimation: Advances and Challenges

A Review of Homography Estimation: Advances and Challenges

Hybrid model of alternating least squares and root polynomial technique for color correction

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