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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

678
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.
678
Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Visual System01:26

Visual System

591
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
591
Focusing of Light in the Eye01:16

Focusing of Light in the Eye

2.9K
Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
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Techniques for Processing Eyes Implanted with a Retinal Prosthesis for Localized Histopathological Analysis: Part 2 Epiretinal Implants with Retinal Tacks
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E2VIDX:改善了传统视觉和生物视觉之间的桥梁.

Xujia Hou1, Feihu Zhang1, Dhiraj Gulati2

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'An, China.

Frontiers in neurorobotics
|November 13, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了E2VIDX,这是一种用于事件摄像头图像重建的新型神经网络. E2VIDX 改进了功能融合,并减少了模型尺寸,使事件摄像头能够直接使用现有的视觉算法,具有卓越的性能.

关键词:
深度学习是一种深度学习.动态视觉传感器是一个动态视觉传感器.事件摄像机事件摄像机图像的分类图像的分类.图像重建 图像重建实例细分 实例细分 实例细分对象检测检测对象检测对象检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 生物仿真工程 生物仿真工程

背景情况:

  • 传统摄像机 (RGBD,CMOS,CCD) 在具有挑战性的照明下与运动模糊和曝光作斗争.
  • 事件摄像机,灵感来自生物视觉,提供低延迟,高动态范围,没有动作模糊.
  • 事件摄像头数据需要专门的图像重建,以便与标准视觉算法兼容.

研究的目的:

  • 为事件摄像机开发一个改进的图像重建网络.
  • 与现有方法相比,增强特征融合和减少模型大小.
  • 为实际应用创建一个更通用的事件摄像头重建技术.

主要方法:

  • 设计了E2VIDX,这是一个基于E2VID方法的新神经网络.
  • 集成的组卷积和子像素卷积用于增强功能融合和模型压缩 (25%的尺寸缩小).
  • 开发了一种新的两部分损失函数,针对重建图像的高层和低层特征.

主要成果:

  • 在图像重建质量方面,E2VIDX显著超过了最先进的方法.
  • 在结构相似性 (SSIM) 中实现了1.3%的增加,而在学习感知图像补丁相似性 (LPIPS) 中降低了1.7%.
  • 在GPU和CPU上展示了更快的处理速度,平均平方误差 (MSE) 减少了2.5%.

结论:

  • E2VIDX提供了卓越的事件摄像机图像重建.
  • 网络的效率和性能允许直接应用现有的视觉算法.
  • 从E2VIDX重建的图像在图像分类,对象检测和实例分割任务中成功使用.