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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

625
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.
625
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

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A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
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相关实验视频

Updated: Jun 21, 2025

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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重新定义准确性:用于不规则照明场景的水下深度估计.

Tong Liu1,2, Sainan Zhang1,2, Zhibin Yu1,2

  • 1Key Laboratory of Ocean Observation and Information of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Sanya 572024, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括

这项研究引入了一个新的自主监督网络用于水下深度估计,提高了在具有挑战性的低光和过度曝光条件下的精度. 该方法增强图像,并利用先前的知识来更好地获取3D空间信息.

关键词:
辅助的水下深度信息 辅助的水下深度信息自主监督的网络网络水下图像增强水下图像增强水下单眼深度估计方法

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 海洋学 海洋学 海洋学

背景情况:

  • 水下深度地图对于3D可视化,导航和探索至关重要.
  • 现有的深度估计方法与水下不规则的照明 (低光和过度曝光) 斗争.
  • 照明不足会降低图像对比度和物体纹理,妨碍准确的深度感知.

研究的目的:

  • 为水下环境开发一个改进的自我监督的单眼深度估计网络.
  • 为了应对低光和过度曝光的水下图像所带来的挑战.
  • 为了提高水下3D空间信息采集的准确性和稳定性.

主要方法:

  • 提出了一个水下自我监督的单眼深度估计网络.
  • 集成了一个蒙特卡罗图像增强模块 (MC-IEM),用于低光图像的概率增强.
  • 利用转移学习将从大规模模型中获得的先前知识纳入,以改进深度估计和解决过度暴露.

主要成果:

  • 拟议的网络在两个公共水下数据集上表现出卓越的性能.
  • MC-IEM有效地解决了低光条件下的不确定性,改善了对象识别和距离估计.
  • 转移学习改进了损失函数和深度网络,减轻了过度暴露问题.

结论:

  • 这种新型网络显著提高了水下深度估计的准确性.
  • 图像增强和辅助深度信息的整合为具有挑战性的水下条件提供了强大的解决方案.
  • 这种方法为各种水下应用提供了更可靠的3D空间信息.