Review



5 × 5 kernel size  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc 5 × 5 kernel size
    Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × <t>5</t> <t>kernel</t> size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )
    5 × 5 Kernel Size, 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/result/5 × 5 kernel size/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    5 × 5 kernel size - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images"

    Article Title: Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images

    Journal: Biomedical Optics Express

    doi: 10.1364/BOE.539001

    Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × 5 kernel size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )
    Figure Legend Snippet: Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × 5 kernel size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )

    Techniques Used: Control



    Similar Products

    5 × 5 kernel size  (MathWorks Inc)
    90
    MathWorks Inc 5 × 5 kernel size
    Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × <t>5</t> <t>kernel</t> size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )
    5 × 5 Kernel Size, 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/result/5 × 5 kernel size/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    5 × 5 kernel size - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    merged non-confocal quadrant-detection images processed using matlab 5 × 5 kernel size  (MathWorks Inc)
    90
    MathWorks Inc merged non-confocal quadrant-detection images processed using matlab 5 × 5 kernel size
    Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × <t>5</t> <t>kernel</t> size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )
    Merged Non Confocal Quadrant Detection Images Processed Using Matlab 5 × 5 Kernel Size, 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/result/merged non-confocal quadrant-detection images processed using matlab 5 × 5 kernel size/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    merged non-confocal quadrant-detection images processed using matlab 5 × 5 kernel size - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    convolution filters=10, kernel size=5  (SoftMax Inc)
    90
    SoftMax Inc convolution filters=10, kernel size=5
    Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × <t>5</t> <t>kernel</t> size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )
    Convolution Filters=10, Kernel Size=5, supplied by SoftMax 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/result/convolution filters=10, kernel size=5/product/SoftMax Inc
    Average 90 stars, based on 1 article reviews
    convolution filters=10, kernel size=5 - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    receptive fields laplacian of gaussian kernel size 9 s 5 0.6  (MathWorks Inc)
    90
    MathWorks Inc receptive fields laplacian of gaussian kernel size 9 s 5 0.6
    Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × <t>5</t> <t>kernel</t> size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )
    Receptive Fields Laplacian Of Gaussian Kernel Size 9 S 5 0.6, 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/result/receptive fields laplacian of gaussian kernel size 9 s 5 0.6/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    receptive fields laplacian of gaussian kernel size 9 s 5 0.6 - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × 5 kernel size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )

    Journal: Biomedical Optics Express

    Article Title: Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images

    doi: 10.1364/BOE.539001

    Figure Lengend Snippet: Visibility of cones in confocal and non-confocal AOSLO images. A 160 × 160µm 2 ROI at 7.5° temporal was imaged using quadrant-detection AOSLO in a 35-year-old control (Subject 2). ( A ) Confocal image shows ambiguous cones with intervening rods. In contrast, inner segments of the cones are readily distinguished in non-confocal ( B ) horizontal split-detection and ( C ) vertical split-detection images. Note that the difference in edge contrast of cell boundary and non-homogeneous intensity profile (bright and dark opposing regions) within each cell in different split-detection images. Yellow and red arrows indicate two cones with reduced visibility in horizontal split-detection and vertical split-detection images, respectively. ( D ) Merged non-confocal quadrant-detection images processed using an emboss filter with 5 × 5 kernel size in MATLAB shows enhanced edge definition of cones in the same image. ( Visualization 1 )

    Article Snippet: Automated cone identifications were performed on merged quadrant-detection image processed using MATLAB 5 × 5 kernel size only.

    Techniques: Control