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Showing 1 - 14 results of 14 for search '(( binary task driven optimization algorithm ) OR ( binary image texture segmentation algorithm ))', query time: 0.33s Refine Results
  1. 1
    Hair removal preprocessing using the DullRazor algorithm before image generation.

    Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

    Published 2025
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  2. 2
    Min-Cut/Max-Flow Problem Instances for Benchmarking

    Min-Cut/Max-Flow Problem Instances for Benchmarking by Patrick Møller Jensen (7232042)

    Published 2022
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  3. 3
    Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx

    Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

    Published 2024
    “…Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. …”
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  4. 4
    Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx

    Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx by Bettina Katalin Budai (13951317)

    Published 2022
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  5. 5
    Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX

    Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX by Bettina Katalin Budai (13951317)

    Published 2022
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  6. 6
    Data_Sheet_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.XLSX

    Data_Sheet_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.XLSX by Bettina Katalin Budai (13951317)

    Published 2022
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  7. 7
    Thesis-RAMIS-Figs_Slides

    Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

    Published 2024
    “…In the context of facies recovery using simulations, the task of optimal sampling is formalized and addressed using a maximum information extraction criterion. …”
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  8. 8
    <b>Multimodal MRI radiomics</b><b> based on </b><b>habitat subregions of the tumor microenvironment</b><b> for predicting risk stratification in glioblastoma</b>

    <b>Multimodal MRI radiomics</b><b> based on </b><b>habitat subregions of the tumor microenvironment</b><b> for predicting risk stratification in glioblastoma</b> by Han Wang (21457334)

    Published 2025
    “…</p><p dir="ltr">A fully automated approach involving label fusion from multiple deep learning algorithms was used to segment distinct tumor subregions histologically. …”
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  9. 9
    Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
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  10. 10
    Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
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  11. 11
    Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
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  12. 12
    Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
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  13. 13
    Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
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  14. 14
    Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx

    Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
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