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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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Accessory Structures of the Eye01:17

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Optical perception, or vision, is an extraordinary sense dependent on converting light signals received via the ocular organs. These organs, known as eyes, are securely positioned within the bony cavities of the skull, called orbits. The orbits serve a dual purpose: a protective shield for the ocular globes and a stable attachment point for the soft ocular tissues. The eye's external protective mechanisms include the eyelids, which are edged with lashes that act as a barrier against foreign...
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Collisions in Multiple Dimensions: Introduction01:05

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
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改进基于LGMD的模型,通过双眼结构进行碰撞预测.

Yi Zheng1,2, Yusi Wang1,2, Guangrong Wu1,2

  • 1School of Mathematics and Information Science, Guangzhou University, Guangzhou, China.

Frontiers in neuroscience
|September 21, 2023
PubMed
概括

这项研究引入了一种新的双筒望远镜巨型垂体运动探测器 (LGMD) 模型,用于改进碰撞预测. 改进的模型准确地区分运动模式,并在各种场景中保持稳健性.

关键词:
双眼视力 双眼视力 双眼视力 双眼视力碰撞预测 碰撞预测深度距离深度距离的距离不同的差异差异的差异.球体巨型运动探测器 (LGMDs)

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科学领域:

  • 计算神经科学是一种计算神经科学.
  • 人工智能的人工智能是人工智能.
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 球状巨型运动探测器 (LGMD) 神经元对于检测迫在眉的刺激和预测碰撞至关重要.
  • 现有的基于LGMD的模型缺乏深度感知,并且难以区分运动模式 (靠近,退后,翻译).
  • 当前的模型表现出由于固定值和一般确定过程的性能变化.

研究的目的:

  • 开发基于LGMD的先进模型,包括深度距离,以提高碰撞预测.
  • 提高模型在接近,退缩和翻译运动模式之间区分的能力.
  • 在各种环境条件下提高模型的强度和可靠性.

主要方法:

  • 提出了一种新的双筒望远镜LGMD (Bi-LGMD) 模型.
  • 使用双眼差异计算提取的深度距离.
  • 引入了自我适应的警告深度距离,以提高强度.

主要成果:

  • 双LGMD模型有效地区分了运动模式.
  • 使用模拟和真实世界的视频验证模型的有效性.
  • 在对比度和噪声变化方面表现出强度.

结论:

  • 拟议的双LGMD模型显著改进了现有的基于LGMD的碰撞预测系统.
  • 深度距离和适应值的整合增强了运动模式的区分和整体模型的稳定性.
  • 该模型对需要可靠的视觉感知和避免碰撞的真实世界应用具有前景.