Globally Optimal Rigid Intensity Based Registration: A Fast Fourier Domain Approach

Nasihatkon, Behrooz; Fejne, Frida; Kahl, Fredrik

doi:10.1109/cvpr.2016.639

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Scale invariant feature transform (SIFT) and speed-up robust feature (SURF) are effective image feature point matching algorithms; however, they are computationally complex, and their real-time performance is poor. Rublee et al showed that oriented fast and rotated brief (ORB) has strong robustness as well as a high data-processing speed (one order of magnitude faster than that of the SURF algorithm and two orders of magnitude faster than that of the SIFT algorithm) [40,41], which can partially compensate for the poor robustness of the intensity-based registration method [42,43]. This study adopts a sub-voxel-level high-precision image registration algorithm based on a combination of ORB feature extraction and an intensity-based registration method [44,45].…”

Section: Sub-voxel-level Joint Registration Between Image Bandsmentioning

confidence: 99%

“…The registration process of the algorithm is shown in Figure 1. of the SURF algorithm and two orders of magnitude faster than that of the SIFT algorithm) [40,41], which can partially compensate for the poor robustness of the intensity-based registration method [42,43]. This study adopts a sub-voxel-level high-precision image registration algorithm based on a combination of ORB feature extraction and an intensity-based registration method [44,45].…”

Section: Sub-voxel-level Joint Registration Between Image Bandsmentioning

confidence: 99%

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An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites

Yang

Lei

et al. 2020

Remote Sensing

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Super-resolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve ill-conditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the gray-level variation between adjacent pixels, and the signal-to-noise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSR-NCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified.

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Section: Sub-voxel-level Joint Registration Between Image Bandsmentioning

confidence: 99%

Section: Sub-voxel-level Joint Registration Between Image Bandsmentioning

confidence: 99%

An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites

Yang

Lei

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The transform domain-based registration algorithm transforms images from the spatial domain to the frequency domain and then analyzes the image in the frequency domain to determine the registration parameters. The commonly used frequency domain transform-based image registration algorithms mainly include wavelet transform registration technology [20,21], Fourier transform registration technology [22][23][24] and composite registration algorithm combined with the space and frequency domain [25,26]. This type of image registration method has a strong anti-noise ability, but the calculation amount is relatively large.…”

Section: Introductionmentioning

confidence: 99%

Automatic Feature Region Searching Algorithm for Image Registration in Printing Defect Inspection Systems

Chen

Gao

et al. 2019

Applied Sciences

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Image registration is a key step in printing defect inspection systems based on machine vision, and its accuracy depends on the selected feature regions to a great extent. Aimed at the current problems of low efficiency and crucial errors of human vision and manual selection, this study proposes a new automatic feature region searching algorithm for printed image registration. First, all obvious shapes are extracted in the preliminary shape extraction process. Second, shape searching algorithms based on contour point distribution information and edge gradient direction, respectively, are proposed. The two algorithms are combined to put forward a relatively effective and discriminative feature region searching algorithm that can automatically detect shapes such as quasi-rectangular, oval, and so on, as feature regions. The entire image and the subregional experimental results show that the proposed method can be used to extract ideal shape regions, which can be used as characteristic shape regions for image registration in printing defect detection systems.

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Pork Registration Using Skin Image with Deep Neural Network Features

Song

Cai

Zeng

2019

Artificial Intelligence and Mobile Services – AIMS 2019

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Globally Optimal Rigid Intensity Based Registration: A Fast Fourier Domain Approach

Cited by 3 publications

References 14 publications

An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites

An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites

Automatic Feature Region Searching Algorithm for Image Registration in Printing Defect Inspection Systems

Pork Registration Using Skin Image with Deep Neural Network Features

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