بدائل البحث:
organization algorithm » optimization algorithms (توسيع البحث), maximization algorithm (توسيع البحث), generation algorithm (توسيع البحث)
spatial organization » spatial variation (توسيع البحث)
scale optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), phase optimization (توسيع البحث)
image spatial » scale spatial (توسيع البحث)
organization algorithm » optimization algorithms (توسيع البحث), maximization algorithm (توسيع البحث), generation algorithm (توسيع البحث)
spatial organization » spatial variation (توسيع البحث)
scale optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), phase optimization (توسيع البحث)
image spatial » scale spatial (توسيع البحث)
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Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment
منشور في 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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Sample image for illustration.
منشور في 2024"…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …"
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Quadratic polynomial in 2D image plane.
منشور في 2024"…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …"
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Steps in the extraction of 14 coordinates from the CT slices for the curved MPR.
منشور في 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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Comparison analysis of computation time.
منشور في 2024"…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …"
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Process flow diagram of CBFD.
منشور في 2024"…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …"
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Precision recall curve.
منشور في 2024"…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …"
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DataSheet_1_Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.pdf
منشور في 2022"…Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. …"
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Flow diagram of the automatic animal detection and background reconstruction.
منشور في 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. …"