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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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

Updated: Jul 14, 2026

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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一个基于计算机视觉的框架,用于客观评估沉没的上眼.

Longfei Weng1, Yuchen Shen1, Shiqi Xie2,3

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Scientific reports
|October 22, 2025
PubMed
概括

本研究介绍了一种计算机视觉 (CV) 框架,用于客观地评估沉没的上眼. 该方法从单个图像中分析眼形态,使手术结果能够更好地评估.

关键词:
计算机视觉 计算机视觉 计算机视觉图像评估 图像评估 图像评估沉没的上眼上方的眼.支持矢量机器的支持矢量机器.

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科学领域:

  • 眼科医生 眼科 眼科
  • 计算机视觉 计算机视觉
  • 医疗成像医学成像

背景情况:

  • 沉没的上眼的纠正对于周围轨道重建至关重要.
  • 目前的评估方法依赖于昂贵的设备或主观的评估.
  • 需要客观和可访问的评估工具.

研究的目的:

  • 开发一个计算机视觉 (CV) 框架,用于客观地评估沉没的上眼形态.
  • 提供一种可靠的方法来评估外围轨道重建中的手术结果.

主要方法:

  • 开发了一个两阶段的简历框架.
  • 检测了面部标志,以隔离周眼区域,然后进行正常化和细分.
  • 提取了包括灰色值变异 (VGV),结构相似度指数 (SSIM) 和眼纹程度 (DEW) 在内的关键特征.
  • 一个支持向量机 (SVM) 模型整合了这些特征,以得分整体形态.

主要成果:

  • 在正常和患者组之间观察到VGV,SSIM和DEW的显著差异.
  • 拟议的方法证明了术后手术结果的可测量改善.
  • 在SVM模型输出,L2距离到分离超平面 (D) 的距离,有效地得分了形态特征.

结论:

  • CV框架提供了一个客观和可访问的方法来评估沉没的上眼形态.
  • 这种方法可以帮助评估外围轨道重建中手术干预的有效性.
  • 该方法显示了改善临床决策和患者护理的潜力.