Multi-modal and Multi-temporal Image Registration in the Presence of Gross Outliers Using Feature Voxel-Weighted Normalized Mutual Information

Gu, Zhijun; Qin, Binjie

doi:10.1109/nssmic.2006.353692

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Gu et al(2006) define a normalized joint feature weight map which shows the spatial feature similarity between neighbourhoods of corresponding voxels in overlapped regions of two images. Junli et al (2008) consider different levels of importance for the reference and floating image in order to achieve more robust and accurate registration.…”

Section: Introductionmentioning

confidence: 99%

Automatic Registration of Multi-Source Data Using Mutual Information

Parmehr

Zhang

Fraser

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Automatic image registration is a basic step in multi-sensor data integration in remote sensing and photogrammetric applications such as data fusion. The effectiveness of Mutual Information (MI) as a technique for automated multi-sensor image registration has previously been demonstrated for medical and remote sensing applications. In this paper, a new General Weighted MI (GWMI) approach that improves the robustness of MI to local maxima, particularly in the case of registering optical imagery and 3D point clouds, is presented. Two different methods including a Gaussian Mixture Model (GMM) and Kernel Density Estimation have been used to define the weight function of joint probability, regardless of the modality of the data being registered. The Expectation Maximizing method is then used to estimate parameters of GMM, and in order to reduce the cost of computation, a multi-resolution strategy has been used. The performance of the proposed GWMI method for the registration of aerial orthotoimagery and LiDAR range and intensity information has been experimentally evaluated and the results obtained are presented.

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Section: Introductionmentioning

confidence: 99%

Automatic Registration of Multi-Source Data Using Mutual Information

Parmehr

Zhang

Fraser

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…To solve the outlier problem, we proposed the joint saliency map (JSM) to group the corresponding saliency structures (called Joint Saliency Structures, JSSs) in intensity-based similarity measure computation [31]. The JSM has been proved to greatly improve the accuracy and robustness of rigid [31] [32] and nonrigid [10] [33][34] image registration with outliers. We further think, by reflecting the local structure correspondence, the JSM also could guide the local kernel regression for accu-rately estimating registration transformations in the nonrigid image registration with outliers.…”

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confidence: 99%

Structure matching driven by joint-saliency-structure adaptive kernel regression

Qin

Shen

Zhou

et al. 2016

Applied Soft Computing

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Camera fusion for real-time temperature monitoring of neonates using deep learning

Lyra

Rixen

Heimann

et al. 2022

Med Biol Eng Comput

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The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning–based algorithms and camera modalities such as RGB and infrared thermography can improve the development of cable-free methods for the extraction of vital parameters. In this study, a real-time approach for local extraction of temperatures on the body surface of neonates using a multi-modal clinical dataset was implemented. Therefore, a trained deep learning–based keypoint detector was used for body landmark prediction in RGB. Image registration was conducted to transfer the RGB points to the corresponding thermographic recordings. These landmarks were used to extract the body surface temperature in various regions to determine the central-peripheral temperature difference. A validation of the keypoint detector showed a mean average precision of 0.82. The registration resulted in mean absolute errors of 16.4 px (8.2 mm) for x and 22.4 px (11.2 mm) for y. The evaluation of the temperature extraction revealed a mean absolute error of 0.55 $$^{\circ }$$ ∘ C. A final performance of 31 fps was observed on the NVIDIA Jetson Xavier NX module, which proves real-time capability on an embedded GPU system. As a result, the approach can perform real-time temperature extraction on a low-cost GPU module. Graphical abstract

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Multi-modal and Multi-temporal Image Registration in the Presence of Gross Outliers Using Feature Voxel-Weighted Normalized Mutual Information

Cited by 7 publications

References 17 publications

Automatic Registration of Multi-Source Data Using Mutual Information

Automatic Registration of Multi-Source Data Using Mutual Information

Structure matching driven by joint-saliency-structure adaptive kernel regression

Camera fusion for real-time temperature monitoring of neonates using deep learning

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