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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

602
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.
602

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相关实验视频

Updated: Jun 11, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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Published on: July 21, 2020

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基于双眼合作的无参考立体图像质量评估.

Hanling Wang1, Xiao Ke1, Wenzhong Guo1

  • 1Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, Fujian, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, Fuzhou, 350116, China.

Neural networks : the official journal of the International Neural Network Society
|September 28, 2024
PubMed
概括

这项研究引入了一种新的无参考立体图像质量评估方法 (NR-SIQA). 它使用神经网络和以突出度为导向的裁剪来准确预测图像质量,而无需使用原始引用.

关键词:
图像处理 图像处理神经网络的神经网络的神经网络没有参考的图像质量评估.立体镜图像质量 立体镜图像质量

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科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 人类视觉系统 视觉系统

背景情况:

  • 评估立体图像质量 (SIQA) 是由于双眼视觉复杂性和视界差异的挑战.
  • 现有的方法可能会在多重扭曲图像的质量预测中表现出偏差.

研究的目的:

  • 开发一种无参考SIQA方法来解决质量预测偏差.
  • 研究人类视觉皮层处理,以改善图像质量评估.

主要方法:

  • 为NR-SIQA提出了一个端到端的神经网络.
  • 开发了一个以突出度为导向的图像补丁生成算法,将左和右视图融合为图像裁剪.

主要成果:

  • 这种新方法的性能优于LIVE 3D和WIVC 3D数据库上的最新NR-SIQA指标.
  • 在特定噪声指标上取得了出色的表现.
  • 证明了模型概括能力.

结论:

  • 拟议的NR-SIQA方法有效地评估了没有参考图像的立体图像质量.
  • 以突出为导向的方法提高了质量预测的准确性和稳定性.