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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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.
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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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图像内在基于无监督的单眼深度估计在内镜.

Bojian Li, Bo Liu, Miao Zhu

    IEEE journal of biomedical and health informatics
    |May 14, 2024
    PubMed
    概括

    这项研究引入了一种新方法,用于在微创手术 (MIS) 中使用内在图像分解 (IID) 来进行无监督单眼深度估计. 这种方法提高了用于增强现实导航的3D重建精度.

    科学领域:

    • 计算机视觉 计算机视觉
    • 医疗成像医学成像
    • 手术技术 手术技术

    背景情况:

    • 无监督的单眼深度估计对于微创手术 (MIS) 中的内镜非常重要.
    • 内镜成像挑战了传统的光度一致性假设,阻碍了深度估计.
    • 现有的方法通常依赖于图像预处理,这可能是次优的.

    研究的目的:

    • 开发一种用于内镜中无监督单眼深度估计的新框架.
    • 将内在图像分解 (IID) 与深度学习相结合,以提高深度准确性.
    • 为了克服内镜成像特征所带来的局限性,无需预处理.

    主要方法:

    • 一个新的端到端框架,结合了图像内在分解模块和合成重建模块.
    • 利用IID的白色图表来解决具有挑战性的内镜图像属性.
    • 设计专用损失函数,以基于 IID 集成的强大网络培训.

    主要成果:

    • 提出的基于内在的方法优于SCARED和Hamlyn数据集上的最先进的技术.
    • 验证证实了该方法的概括能力及其组件的有效性.
    • 在内镜图像的无监督单眼深度估计中取得了卓越的结果.

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    结论:

    • 基于内在的无监督单眼深度学习框架为MIS提供了一个开创性的解决方案.
    • 这种方法提高了增强现实导航的3D重建质量和稳定性.
    • 这种方法有效地通过IID绕过了具有挑战性的内镜成像特征.