An improvement of image registration based on phase correlation

Ge, Peng; Lan, Caiyu; Wang, Hong

doi:10.1016/j.ijleo.2014.07.086

Cited by 18 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we applied the Fourier-Mellin Transform (FMT) to the registration of images. The FMT introduces polar coordinate transformation into the phase correlation method, which can realize fast registration of two images with rotation, zoom, and translation [19,20]. The principle of FMT is as follows.…”

Section: Image Registrationmentioning

confidence: 99%

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

Wang

et al. 2023

Forests

View full text Add to dashboard Cite

The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria sinensis using multispectral imaging was proposed. First, image registration and segmentation were performed using the Fourier–Mellin transform (FFT) and the fuzzy local information c-means clustering algorithm (FLICM). Second, the spectral features (SFs), texture features (TFs), and comprehensive features (CFs) of the image were extracted. Third, using the eigenvectors of the SFs, TFs, and CFs as input, a random forest regression model for estimating the water content of A. sinensis was constructed, respectively. Finally, the monarch butterfly optimization (MBO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA) were used to optimize all models to determine the best estimation model. The results showed that: (1) 60%–80% soil water content is the most suitable for A. sinensis growth. Compared with waterlogging, drought inhibited A. sinensis growth more significantly. (2) FMT + FLICM could achieve rapid segmentation of discrete A. sinensis multispectral images on the basis of guaranteed accuracy. (3) The prediction effect of TFs was basically the same as that of SFs, and the prediction effect of CFs was higher than that of SFs and TFs, but this difference would decrease with the optimization of the RFR model. (4) Among all models, SSA-RFR_CFs had the highest accuracy, with an R2 of 0.8282. These results confirmed the feasibility and accuracy of applying multispectral imaging technology to estimate the water content of A. sinensis and provide a reference for the protection and cultivation of endangered precious tree species.

show abstract

Section: Image Registrationmentioning

confidence: 99%

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

Wang

et al. 2023

Forests

View full text Add to dashboard Cite

show abstract

“…A coarse mask was manually selected to encompass the whole heart and used as input to the registration step. Then a Fourier-based cross-correlation method was applied to iteratively register neighboring frames [41]. Model-based deformable registration was then applied to perfusion images as in [42].…”

Section: Post-processingmentioning

confidence: 99%

Quantitative Myocardial Perfusion with 2D and 3D Sequences Alternating Every Heartbeat

Huang

Tian

Mendes

et al. 2021

Preprint

View full text Add to dashboard Cite

PurposeTo evaluate a myocardial perfusion acquisition that alternates 2D simultaneous multi-slice (SMS) and 3D stack-of-stars (SoS) acquisitions each heartbeat. MethodsA hybrid saturation recovery radial 2D SMS and a saturation recovery 3D SoS sequence were created for the quantification of myocardial blood flow (MBF). Initial studies were done to study the effects of using only every other beat for the 2D SMS in two subjects, and for the 3D SoS in two subjects. Alternating heartbeat 2D SMS and 3D SoS were then performed in ten dog studies at rest, four dog studies at adenosine stress, and two human resting studies. 2D SMS acquisition acquired three slices and 3D SoS acquired six slices. An arterial input function (AIF) for 2D SMS was obtained using the first 24 rays. For 3D, the AIF was obtained in a 2D slice prior to each 3D SoS readout. Quantitative MBF analysis was performed for 2D SMS and 3D SoS separately, using a two-compartment model. ResultsAcquiring every-other-beat data resulted in 5-20% perfusion changes at rest for both 2D SMS and 3D SoS methods. For alternating acquisitions, 2D SMS and 3D SoS quantitative perfusion values were comparable for both the twelve rest studies (2D SMS: 0.68±0.15 vs 3D: 0.69±0.15 ml/g/min, p=0.85) and the four stress studies (2D SMS: 1.28±0.22 vs 3D: 1.30±0.24 ml/g/min, p=0.66).ConclusionEvery-other-beat acquisition changed estimated perfusion values relatively little for both sequences. 2D SMS and 3D SoS gave similar quantitative perfusion estimates when used in an alternating every-other-heartbeat acquisition. Such an approach allows consideration of more diverse perfusion acquisitions that could have complementary features, although testing in a cardiac disease population is needed.

show abstract

“…3 The border of image is regarded as sharp discontinuities because of the periodicity of Fourier transformation, resulting in high frequencies in frequency domain [29]. Flat-top function is used to weaken the interference of the border, which is a 2D improved window function [34]. Frequency masking is also used as a small trick to filter out the frequencies contaminated by aliasing [35].…”

Section: Algorithm Descriptionmentioning

confidence: 99%

A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

Yang

Zhao

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

show abstract

An improvement of image registration based on phase correlation

Cited by 18 publications

References 10 publications

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

Quantitative Myocardial Perfusion with 2D and 3D Sequences Alternating Every Heartbeat

A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

Contact Info

Product

Resources

About