関連する実験動画

Updated: Jan 7, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.2K

GrowSP++: 監視されていない3Dセマンティックセグメンテーションのための成長スーパーポイントとプリミティブ

Zihui Zhang, Weisheng Dai, Bing Wang

    IEEE transactions on pattern analysis and machine intelligence
    |January 2, 2026
    PubMed
    まとめ

    No abstract available in PubMed .

    さらに関連する動画

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    25.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    721

    関連する実験動画

    Last Updated: Jan 7, 2026

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    10.2K
    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    25.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    721

    関連する概念動画

    Depth Perception and Spatial Vision01:15

    Depth Perception and Spatial Vision

    1.7K
    Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
    1.7K
    JoVE
    x logofacebook logolinkedin logoyoutube logo
    JoVEについて
    概要リーダーシップブログJoVEヘルプセンター
    著者向け
    出版プロセス編集委員会範囲と方針査読よくある質問投稿
    図書館員向け
    推薦の声購読アクセスリソース図書館諮問委員会よくある質問
    研究
    JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
    教育
    JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
    利用規約
    プライバシーポリシー
    ポリシー
    Jove
    Visualize
    お問い合わせ