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Showing 1 - 5 results of 5 for search '(( binary a pose estimation algorithm ) OR ( binary basic driven optimization algorithm ))', query time: 0.54s Refine Results
  1. 1
    Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model.

    Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

    Published 2023
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  2. 2
    Experimental setup and trajectory estimation methodology.

    Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

    Published 2023
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  3. 3
    ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for test data classification results across all folds.

    ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

    Published 2025
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  4. 4
    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
    “…The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. …”
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  5. 5
    GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios

    GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

    Published 2025
    “…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”
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