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linear 2d interpolation included in the matlab r2015b software package  (MathWorks Inc)


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    MathWorks Inc linear 2d interpolation included in the matlab r2015b software package
    Linear 2d Interpolation Included In The Matlab R2015b Software Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/2d+linear+interpolation/10__3390_slash_jmse10030401-117-14-19
    Average 90 stars, based on 1 article reviews
    linear 2d interpolation included in the matlab r2015b software package - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Self-motion illusions from distorted optic flow in multifocal glasses
    Article Snippet: As a mathematical description of the distortion, we define a function F , which transforms a point ( x , y ) in the undistorted image plane to the point ( x d , y d ) in the distorted image, i.e. the point at which it would appear when looking through the lens. (Equation 1) ( x d y d ) = ( F x ( x , y ) F y ( x , y ) ) The two components of F and their derivatives were interpolated from a 21 × 21 ray traced grid using 2D linear interpolation in Matlab (The Mathworks Inc, Natick, MA, USA).



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    linear 2d interpolation included in the matlab r2015b software package  (MathWorks Inc)
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    2d linear interpolation  (MathWorks Inc)
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    2d linear interpolation interp2.m  (MathWorks Inc)
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    MathWorks Inc 2d linear interpolation interp2.m
    Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by <t>2D</t> spatial <t>interpolation</t> (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.
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    linear 2d interpolation using matlab's interp2 function  (MathWorks Inc)
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    MathWorks Inc linear 2d interpolation using matlab's interp2 function
    Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by <t>2D</t> spatial <t>interpolation</t> (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.
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    Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by 2D spatial interpolation (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.

    Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control

    Article Title: Improved Super-Resolution Ultrasound Microvessel Imaging with Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking

    doi: 10.1109/TUFFC.2017.2778941

    Figure Lengend Snippet: Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by 2D spatial interpolation (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.

    Article Snippet: The data was then spatially interpolated to 0.02 mm resolution using 2D linear interpolation (the “interp2.m” function in Matlab), as shown in .

    Techniques: Derivative Assay