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gaussian blur kernel using the matlab "imgaussfilt" function  (MathWorks Inc)


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    MathWorks Inc gaussian blur kernel using the matlab "imgaussfilt" function
    Gaussian Blur Kernel Using The Matlab "Imgaussfilt" Function, 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/gaussian+kernel+function/pmc10910559-78-28-27
    Average 90 stars, based on 1 article reviews
    gaussian blur kernel using the matlab "imgaussfilt" function - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    other:

    Article Title: The relationship between climate classes and particulate matters over Europe.
    Article Snippet: The following built-in MATLAB functions imgaussfilt, kmeans, trenddecomp and findpeaks were used for 2D data smoothing, clustering, trend decomposition and searching for extreme values, respectively.

    Article Title: Cross-Species Biomechanical Determinants of Shape Diversity
    Article Snippet: The process involved the following steps: Gaussian smoothing was applied using the MATLAB function ‘imgaussfilt’, followed by thresholding, and morphological operations like opening (‘imopen’) and closing (‘imclose’) with parameters tailored to each image’s properties to generate binary masks.

    Article Title: SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network
    Article Snippet: All of the Gaussian filters used in this study are in two dimensions and were created using the MATLAB library named “imgaussfilt,” which is mainly defined for two-dimensional objects like images.

    Article Title: The relationship between climate classes and particulate matters over Europe
    Article Snippet: The following built-in MATLAB functions imgaussfilt , kmeans , trenddecomp and findpeaks were used for 2D data smoothing, clustering, trend decomposition and searching for extreme values, respectively.

    Standard Deviation:

    Article Title: Methods and apparatuses for treating stroke using low-frequency stimulation
    Article Snippet: .. Voltages were spatially smoothed (imgaussfilt in Matlab with a standard deviation of 0.5). .. The gradient function in Matlab was then used to compute the voltage gradient (V/m) in the horizontal and vertical direction across the grid.

    Article Title: Stability and Adaptability in Balance: A Dual Mechanism for Metaplasticity in Cortical Networks
    Article Snippet: .. Finally, we normalized all to the W1P1 condition specific to each population and calculated the relative change Relative values were sorted in matrices (rows with fixed W x , columns with fixed Py ) which we smoothed applying the 2-D Gaussian smoothing kernel to each matrix M pop using the imgaussfilt(M, σ) function in Matlab, where σ = 1.5 specified the standard deviation of the square kernel. ..

    Article Title: Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition
    Article Snippet: Constructing digital twins for predictive tumor treatment response models can have a high computational demand that presents a practical barrier for their clinical adoption.. In this work, we demonstrate that proper orthogonal decomposition, by which a low-dimensional representation of the full model is constructed, can be used to dramatically reduce the computational time required to calibrate a partial differential equation model to magnetic resonance imaging (MRI) data for rapid predictions of tumor growth and response to chemotherapy.. In the proposed formulation, the reduction basis is based on each patient’s own MRI data and controls the overall size of the “reduced order model”.

    Article Title: Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning.
    Article Snippet: Objective: Bone diseases deteriorate the microstructure of bone tissue.. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial resolution of imaging bone tissues.. However, the spatiotemporal trade-off limits the application of OR-PAM.



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