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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

900
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.
900
Differential Leveling01:12

Differential Leveling

308
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
308
Deconvolution01:20

Deconvolution

251
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
251

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相关实验视频

Updated: Sep 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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通过双视图差异估计增强仪器细分网络

Yongming Yang, Zhaoshuo Diao, Ziliang Song

    IEEE journal of biomedical and health informatics
    |August 20, 2025
    PubMed
    概括

    这项研究介绍了EISegNet,一个用于机器人辅助手术仪器细分的新框架. 通过整合深度估计和边缘功能增强,提高手术自动化和安全性,提高准确性.

    科学领域:

    • 计算机视觉
    • 机器人技术
    • 医学成像

    背景情况:

    • 对于机器人辅助手术而言,内镜仪器的精确细分至关重要,可实现精确的导航和自动化.
    • 现有的单眼方法由于复杂的环境,仪器组织相似性和照明变化而难以进行仪器细分.
    • 仪器的不同深度分布,经常被忽视,为改进细分提供了一个关键特征.

    研究的目的:

    • 开发一个先进的框架,EISegNet,以加强内镜仪器的细分.
    • 通过将仪器细分与差异估计相结合,利用多任务学习.
    • 在不同手术场景中提高细分方法的稳定性和通用性.

    主要方法:

    • 提出了EISegNet,一个多任务框架,整合了仪器细分和辅助差异估计.
    • 在细分和差异任务之间实现非对称的交叉注意力机制.
    • 适应立体差异估计用于双视图深度估计,并纳入高斯加权损失函数以强调边缘特征.

    主要成果:

    • 在仪器细分方面实现了5.97%的交叉与欧盟 (IoU) 的增长.
    • 在广泛的交叉数据集实验中表现出卓越的准确性和概括性.
    • 在临床数据集的定性评估中展示了有前途的表现.

    更多相关视频

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    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

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    相关实验视频

    Last Updated: Sep 10, 2025

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    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K
    Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
    08:04

    Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

    Published on: December 4, 2013

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    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

    Published on: July 21, 2020

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    结论:

    • 通过结合深度和边缘信息,EISegNet有效地提高了内镜仪器细分的准确性.
    • 多任务框架和新的损失功能提高了在具有挑战性的外科条件下的性能.
    • 该方法显示了在实际临床应用中推进外科自动化和安全性的巨大潜力.