Aggregation of Convolutional Neural Network Estimations of Homographies by Color Transformations of the Inputs

Molina‐Cabello, Miguel A.; Elizondo, David; Luque‐Baena, Rafael Marcos; López‐Rubio, Ezequiel

doi:10.1109/access.2020.2990744

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…The authors in [35] attempted to build a novel method to calculate homography estimation. A convolutional neural network (CNN) was used to approximate homography, and input pair of images in a set of different versions were given.…”

Section: Literature Surveymentioning

confidence: 99%

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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Section: Literature Surveymentioning

confidence: 99%

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

et al. 2023

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“…Over the years, several successful variants of the RANSAC algorithm for homography estimation have been suggested [9][10][11]. Alternatively, computational approaches such as neural networks and evolutionary computation have also been explored for homography estimation obtaining fruitful results [12][13][14]. Nevertheless, all these methods still require an accurate specification of several point correspondences and a minimum of outliers.…”

Section: Introductionmentioning

confidence: 99%

Homography estimation from a single-point correspondence using template matching and particle swarm optimization

Díaz-Ramírez

Juarez-Salazar²,

Zheng

et al. 2022

Appl. Opt.

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Existing feature-based methods for homography estimation require several point correspondences in two images of a planar scene captured from different perspectives. These methods are sensitive to outliers, and their effectiveness depends strongly on the number and accuracy of the specified points. This work presents an iterative method for homography estimation that requires only a single-point correspondence. The homography parameters are estimated by solving a search problem using particle swarm optimization, by maximizing a match score between a projective transformed fragment of the input image using the estimated homography and a matched filter constructed from the reference image, while minimizing the reprojection error. The proposed method can estimate accurately a homography from a single-point correspondence, in contrast to existing methods, which require at least four points. The effectiveness of the proposed method is tested and discussed in terms of objective measures by processing several synthetic and experimental projective transformed images.

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Alternate Least Square and Root Polynomial Based Colour-Correction Method for High Dimensional Environment

Babbar,

Bajaj

2024

Lecture Notes in Electrical Engineering

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Aggregation of Convolutional Neural Network Estimations of Homographies by Color Transformations of the Inputs

Cited by 6 publications

References 29 publications

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

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

Homography estimation from a single-point correspondence using template matching and particle swarm optimization

Alternate Least Square and Root Polynomial Based Colour-Correction Method for High Dimensional Environment

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