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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

508
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.
508
Perceptual Constancy01:12

Perceptual Constancy

317
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
317
Vision01:24

Vision

52.9K
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.
52.9K

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相关实验视频

Updated: May 24, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

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Published on: April 11, 2025

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基于人类视觉感知机制的进步知识传输网络,用于无参考点云质量评估.

Honglei Su, Yiyun Liu, Qi Liu

    IEEE transactions on visualization and computer graphics
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    我们开发了PKT-PCQA,这是一个新的深度学习网络,用于在没有参考数据的情况下评估点云质量. 这种方法准确地预测了感知质量,超过了现有的技术.

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    相关实验视频

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    Published on: April 11, 2025

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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
    • 信号处理 信号处理

    背景情况:

    • 点云感知质量评估对于压缩和通信等应用程序至关重要.
    • 现有的方法往往需要参考数据,或者在预测人类感知时缺乏准确性.

    研究的目的:

    • 提出PKT-PCQA,一个无参考的深度学习网络,用于准确的点云质量评估.
    • 模拟人类视觉系统,以提高质量预测.

    主要方法:

    • 开发了一个基于点的,无参考的深度学习网络 (PKT-PCQA).
    • 采用渐进的知识转移来进行粗细的质量预测.
    • 利用局部和全球特征提取与空间和通道注意力机制.

    主要成果:

    • 通过PKT-PCQA,比现有的无参考和减少参考方法表现出更高的性能.
    • 在独立数据集上实现了与最先进的全参考方法可比的性能.
    • 在三个大规模点云质量评估数据集中验证了有效性.

    结论:

    • 对于无参考点云质量评估,PKT-PCQA提供了一个强大而准确的解决方案.
    • 拟议的网络有效地模拟人类的视觉感知,以进行质量预测.
    • 这项工作推动了对各种应用的点云质量评估领域的发展.