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Showing 21 - 40 results of 239 for search '(((("element tree algorithms") OR ("element rl algorithm"))) OR ("image processing algorithm"))', query time: 0.73s Refine Results
  1. 21
    Image processing algorithm developed for the identification <i>of Schistosoma haematobium</i> eggs in urine sediment based on image capture on-chip using simple webcam.

    Image processing algorithm developed for the identification <i>of Schistosoma haematobium</i> eggs in urine sediment based on image capture on-chip using simple webcam. by Ewert Linder (376141)

    Published 2013
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  2. 22
    CONSORT flow diagram of OCT imaging observations on flap creation methods for sub-Bowman′s keratomileusis (SBK).

    CONSORT flow diagram of OCT imaging observations on flap creation methods for sub-Bowman′s keratomileusis (SBK). by Zhe Xu (127066)

    Published 2015
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  3. 23
    The Impact of Flap Creation Methods for Sub-Bowman’s Keratomileusis (SBK) on the Central Thickness of Bowman’s Layer

    The Impact of Flap Creation Methods for Sub-Bowman’s Keratomileusis (SBK) on the Central Thickness of Bowman’s Layer by Zhe Xu (127066)

    Published 2015
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  4. 24
    UHR-OCT measured changes of central corneal sublayer thicknesses between visits after sub-Bowman′s keratomileusis (SBK).

    UHR-OCT measured changes of central corneal sublayer thicknesses between visits after sub-Bowman′s keratomileusis (SBK). by Zhe Xu (127066)

    Published 2015
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  5. 25
    UHR-OCT images taken before and after sub-Bowman′s keratomileusis (SBK) surgery in the Moria and FEMTO groups.

    UHR-OCT images taken before and after sub-Bowman′s keratomileusis (SBK) surgery in the Moria and FEMTO groups. by Zhe Xu (127066)

    Published 2015
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  6. 26
    Thickness changes in the corneal sublayers of the Moria and FEMTO groups.

    Thickness changes in the corneal sublayers of the Moria and FEMTO groups. by Zhe Xu (127066)

    Published 2015
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  7. 27
    UHR-OCT measurements of central corneal sublayer thicknesses before and after sub-Bowman′s keratomileusis (SBK).

    UHR-OCT measurements of central corneal sublayer thicknesses before and after sub-Bowman′s keratomileusis (SBK). by Zhe Xu (127066)

    Published 2015
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  8. 28
    UHR-OCT image illustration of the image processing procedure.

    UHR-OCT image illustration of the image processing procedure. by Zhe Xu (127066)

    Published 2015
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  9. 29
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
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  10. 30
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
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  11. 31
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
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  12. 32
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
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  13. 33
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
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  14. 34
    A representative HeLa image, semantic segmentation results overlaid on the filtered image and the ground truth.

    A representative HeLa image, semantic segmentation results overlaid on the filtered image and the ground truth. by Cefa Karabağ (8320152)

    Published 2020
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  15. 35
    Graphical illustration of the five algorithms compared in this work.

    Graphical illustration of the five algorithms compared in this work. by Cefa Karabağ (8320152)

    Published 2020
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  16. 36
    S1 Code -

    S1 Code - by Cefa Karabağ (8320152)

    Published 2020
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  17. 37
    Illustration of the pixel-based metrics.

    Illustration of the pixel-based metrics. by Cefa Karabağ (8320152)

    Published 2020
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  18. 38
    Illustration of the effects of low-pass filtering on one representative image of a HeLa cell.

    Illustration of the effects of low-pass filtering on one representative image of a HeLa cell. by Cefa Karabağ (8320152)

    Published 2020
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  19. 39
    Illustration of intermediate steps of the proposed segmentation algorithm.

    Illustration of intermediate steps of the proposed segmentation algorithm. by Cefa Karabağ (8320152)

    Published 2020
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  20. 40
    Metrics used for comparison between image-processing algorithm and four pre-trained deep learning architectures—VGG16, ResNet18, Inception-ResNet-v2, and U-Net for semantic segmentation of HeLa cell imaged with Electron Microscopy (EM).

    Metrics used for comparison between image-processing algorithm and four pre-trained deep learning architectures—VGG16, ResNet18, Inception-ResNet-v2, and U-Net for semantic segmentation of HeLa cell imaged with Electron Microscopy (EM). by Cefa Karabağ (8320152)

    Published 2020
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