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calibration results  (MathWorks Inc)


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    Structured Review

    MathWorks Inc calibration results
    Calculation process of the A/N ratio using <t>MATLAB</t> and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.
    Calibration Results, 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/calibration results/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    calibration results - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea"

    Article Title: Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

    Journal: Nature and Science of Sleep

    doi: 10.2147/NSS.S492146

    Calculation process of the A/N ratio using MATLAB and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.
    Figure Legend Snippet: Calculation process of the A/N ratio using MATLAB and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.

    Techniques Used:

    Scatterplot of the Mann–Whitney U -test for A/N ratio evaluations. ( A ) Each scatter point represents an individual A/N ratio value assessed by the two experts. ( B ) Each scatter point corresponds to an individual A/N ratio value calculated by the MATLAB algorithm, based on calibration performed by the same two experts. This visual representation offers a comprehensive comparison between the expert evaluations and the MATLAB-calculated A/N ratio values.
    Figure Legend Snippet: Scatterplot of the Mann–Whitney U -test for A/N ratio evaluations. ( A ) Each scatter point represents an individual A/N ratio value assessed by the two experts. ( B ) Each scatter point corresponds to an individual A/N ratio value calculated by the MATLAB algorithm, based on calibration performed by the same two experts. This visual representation offers a comprehensive comparison between the expert evaluations and the MATLAB-calculated A/N ratio values.

    Techniques Used: MANN-WHITNEY, Comparison

    Confusion matrix of adenoid hypertrophy degree performance for deep learning method and MATLAB algorithm. Adenoid hypertrophy degree is classified into three categories: small (A/N ratio 0–50%), medium (A/N ratio 50–75%), and large (A/N ratio 75–100%). In each confusion matrix, the horizontal axis represents the MATLAB results (actual class), while the vertical axis represents the deep learning results (predicted class).
    Figure Legend Snippet: Confusion matrix of adenoid hypertrophy degree performance for deep learning method and MATLAB algorithm. Adenoid hypertrophy degree is classified into three categories: small (A/N ratio 0–50%), medium (A/N ratio 50–75%), and large (A/N ratio 75–100%). In each confusion matrix, the horizontal axis represents the MATLAB results (actual class), while the vertical axis represents the deep learning results (predicted class).

    Techniques Used:



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    Calculation process of the A/N ratio using <t>MATLAB</t> and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.
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    Image Search Results


    Calculation process of the A/N ratio using MATLAB and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.

    Journal: Nature and Science of Sleep

    Article Title: Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

    doi: 10.2147/NSS.S492146

    Figure Lengend Snippet: Calculation process of the A/N ratio using MATLAB and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.

    Article Snippet: Additionally, MATLAB calibration results demonstrated a p value of 0.679, indicating no significant differences between their outcomes.

    Techniques:

    Scatterplot of the Mann–Whitney U -test for A/N ratio evaluations. ( A ) Each scatter point represents an individual A/N ratio value assessed by the two experts. ( B ) Each scatter point corresponds to an individual A/N ratio value calculated by the MATLAB algorithm, based on calibration performed by the same two experts. This visual representation offers a comprehensive comparison between the expert evaluations and the MATLAB-calculated A/N ratio values.

    Journal: Nature and Science of Sleep

    Article Title: Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

    doi: 10.2147/NSS.S492146

    Figure Lengend Snippet: Scatterplot of the Mann–Whitney U -test for A/N ratio evaluations. ( A ) Each scatter point represents an individual A/N ratio value assessed by the two experts. ( B ) Each scatter point corresponds to an individual A/N ratio value calculated by the MATLAB algorithm, based on calibration performed by the same two experts. This visual representation offers a comprehensive comparison between the expert evaluations and the MATLAB-calculated A/N ratio values.

    Article Snippet: Additionally, MATLAB calibration results demonstrated a p value of 0.679, indicating no significant differences between their outcomes.

    Techniques: MANN-WHITNEY, Comparison

    Confusion matrix of adenoid hypertrophy degree performance for deep learning method and MATLAB algorithm. Adenoid hypertrophy degree is classified into three categories: small (A/N ratio 0–50%), medium (A/N ratio 50–75%), and large (A/N ratio 75–100%). In each confusion matrix, the horizontal axis represents the MATLAB results (actual class), while the vertical axis represents the deep learning results (predicted class).

    Journal: Nature and Science of Sleep

    Article Title: Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

    doi: 10.2147/NSS.S492146

    Figure Lengend Snippet: Confusion matrix of adenoid hypertrophy degree performance for deep learning method and MATLAB algorithm. Adenoid hypertrophy degree is classified into three categories: small (A/N ratio 0–50%), medium (A/N ratio 50–75%), and large (A/N ratio 75–100%). In each confusion matrix, the horizontal axis represents the MATLAB results (actual class), while the vertical axis represents the deep learning results (predicted class).

    Article Snippet: Additionally, MATLAB calibration results demonstrated a p value of 0.679, indicating no significant differences between their outcomes.

    Techniques:

    The workflow of the proposed calibration method for a low-cost camera.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: The workflow of the proposed calibration method for a low-cost camera.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    The resampling method from the original calibration fixture image.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: The resampling method from the original calibration fixture image.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    The 2D diffused reflective planar calibration board and circular targets.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: The 2D diffused reflective planar calibration board and circular targets.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Radial distortion profiles at different radial distances for Canon in the air calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Radial distortion profiles at different radial distances for Canon in the air calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Decentering distortion profiles at different radial distances for Canon in the air calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Decentering distortion profiles at different radial distances for Canon in the air calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    In-plane distortions for Canon in the air calibrated using the conventional method and the proposed method at different calibrations: ( a ) the conventional method (the first calibration); ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: In-plane distortions for Canon in the air calibrated using the conventional method and the proposed method at different calibrations: ( a ) the conventional method (the first calibration); ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Radial distortion profiles at different radial distances for GoPro in the air calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Radial distortion profiles at different radial distances for GoPro in the air calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Decentering distortion profiles at different radial distances for GoPro in the air calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Decentering distortion profiles at different radial distances for GoPro in the air calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    In-plane distortions at different calibrations for GoPro in the air: ( a ) the first calibration; ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: In-plane distortions at different calibrations for GoPro in the air: ( a ) the first calibration; ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    The calibrated IO parameters of the two cameras with the conventional method and the proposed method in the air and underwater environment.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: The calibrated IO parameters of the two cameras with the conventional method and the proposed method in the air and underwater environment.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Radial distortion profiles at different radial distances for GoPro in the underwater environment (0.14 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Radial distortion profiles at different radial distances for GoPro in the underwater environment (0.14 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Decentering distortion profiles at different radial distances for GoPro in the underwater environment (0.14 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Decentering distortion profiles at different radial distances for GoPro in the underwater environment (0.14 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    In-plane distortions at different calibrations for GoPro in the underwater environment (0.14 m away from the calibration board): ( a ) the first calibration; ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: In-plane distortions at different calibrations for GoPro in the underwater environment (0.14 m away from the calibration board): ( a ) the first calibration; ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Radial distortion profiles at different radial distances for GoPro in the underwater environment (0.5 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Radial distortion profiles at different radial distances for GoPro in the underwater environment (0.5 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Decentering distortion profiles at different radial distances for GoPro in the underwater environment (0.5 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Decentering distortion profiles at different radial distances for GoPro in the underwater environment (0.5 m away from the calibration board) calibrated using the conventional method and the proposed method at different calibrations.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    In−plane distortions at different calibrations for GoPro in the underwater environment (0.5 m away from the calibration board): ( a ) the first calibration; ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: In−plane distortions at different calibrations for GoPro in the underwater environment (0.5 m away from the calibration board): ( a ) the first calibration; ( b ) the second calibration; ( c ) the third calibration; ( d ) the fourth calibration.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques:

    Measurement accuracy comparison between the conventional camera  calibration  method and the proposed camera  calibration  method.

    Journal: Sensors (Basel, Switzerland)

    Article Title: A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

    doi: 10.3390/s23042041

    Figure Lengend Snippet: Measurement accuracy comparison between the conventional camera calibration method and the proposed camera calibration method.

    Article Snippet: From the GoPro underwater calibration results ( , and , ), the calibrated maximum radial, decentering, and in-plane distortion are 0.11 mm, 0.002 mm, and 25 μm, respectively.

    Techniques: Comparison