Showing 1 - 9 results of 9 for search '(( binary based tracking optimization algorithm ) OR ( binary game model optimization algorithm ))', query time: 0.57s Refine Results
  1. 1
  2. 2
  3. 3

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

    Published 2024
    “…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …”
  4. 4

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

    Published 2024
    “…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …”
  5. 5

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

    Published 2024
    “…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …”
  6. 6

    Precision recall curve. by Indhumathi S. (19173013)

    Published 2024
    “…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …”
  7. 7

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

    Published 2024
    “…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …”
  8. 8

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

    Published 2020
    “…(E) The threshold value is calculated based on the histogram: it is the mean of the image subtracted by 4 (optimal value defined by trial and error). …”
  9. 9

    Supplementary Material 8 by Nishitha R Kumar (19750617)

    Published 2025
    “…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”