Search alternatives:
estimation algorithm » optimization algorithms (Expand Search), maximization algorithm (Expand Search), detection algorithm (Expand Search)
based optimization » whale optimization (Expand Search)
binary imaging » binary image (Expand Search)
imaging pose » imaging probe (Expand Search), imaging probes (Expand Search), imaging based (Expand Search)
binary some » binary isomers (Expand Search)
some based » home based (Expand Search), score based (Expand Search), slope based (Expand Search)
Showing 1 - 11 results of 11 for search '(( binary imaging pose estimation algorithm ) OR ( binary some based optimization algorithm ))', query time: 0.53s Refine Results
  1. 1
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

    Published 2023
    Save to List
    Saved in:
  2. 2
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... by Olaide N. Oyelade (14047002)

    Published 2023
    Save to List
    Saved in:
  3. 3
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

    Published 2023
    Save to List
    Saved in:
  4. 4
    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
    Save to List
    Saved in:
  5. 5
    Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses

    Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

    Published 2025
    Save to List
    Saved in:
  6. 6
    Experimental setup and trajectory estimation methodology.

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

    Published 2023
    Save to List
    Saved in:
  7. 7
    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF

    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF by Maya Khatun (7437011)

    Published 2019
    “…This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. …”
    Save to List
    Saved in:
  8. 8
    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
    Save to List
    Saved in:
  9. 9
    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 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. …”
    Save to List
    Saved in:
  10. 10
    UK Biobank data wrangling statistics and results.

    UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

    Published 2020
    Save to List
    Saved in:
  11. 11
    Seed mix selection model

    Seed mix selection model by Bethanne Bruninga-Socolar (10923639)

    Published 2022
    “…On some extensions to GA package: hybrid optimization, parallelization and islands evolution. …”
    Save to List
    Saved in:

Search Tools: