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Showing 1 - 18 results of 18 for search '(( binary ms based optimization algorithm ) OR ( binary image scale optimization algorithm ))', query time: 0.49s Refine Results
  1. 1
    One advantage of our algorithm is that it is not binary in nature.

    One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

    Published 2022
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  2. 2
    Several loop patterns found in binary code.

    Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

    Published 2020
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  3. 3
    Sample image for illustration.

    Sample image for illustration. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  4. 4
    Quadratic polynomial in 2D image plane.

    Quadratic polynomial in 2D image plane. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  5. 5
    Comparison analysis of computation time.

    Comparison analysis of computation time. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  6. 6
    Process flow diagram of CBFD.

    Process flow diagram of CBFD. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  7. 7
    Precision recall curve.

    Precision recall curve. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  8. 8
    Validation of image processing steps and metric results using images of a microfluidic device.

    Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

    Published 2021
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  9. 9
    tUbenet Foundation Model Weights

    tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

    Published 2025
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  10. 10
    Flowchart of software workflow.

    Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

    Published 2021
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  11. 11
    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

    Published 2019
    “…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”
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  12. 12
    Experimental observation of gastruloids and shape quantification using LOCO-EFA.

    Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

    Published 2024
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  13. 13
    Fig 3 -

    Fig 3 - by Ali Ahmed (2567584)

    Published 2020
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  14. 14
    Steps in the extraction of 14 coordinates from the CT slices for the curved MPR.

    Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. by Linus Woitke (22783534)

    Published 2025
    “…The image is then cleaned in c) using morphological filtering with an <i>opening</i> operation to remove small-scale noise. …”
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  15. 15
    Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF

    Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

    Published 2021
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  16. 16
    Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF

    Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

    Published 2021
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  17. 17
    Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF

    Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

    Published 2021
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  18. 18
    Flow diagram of the automatic animal detection and background reconstruction.

    Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

    Published 2020
    “…If the identical blob that was detected in panel J (bottom) is found in any of the new subtracted binary images (cyan arrow), the animal is considered as having left its original position, and the algorithm continues. …”
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