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从非结构化的摄像机阵列中进行半监督图像拼接.

Erman Nghonda Tchinda1, Maximillian Kealoha Panoff1, Danielle Tchuinkou Kwadjo1

  • 1Department of Electrical and Computer Engineering, The Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611-6200, USA.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了使用SandFall算法和卷积神经网络进行图像拼接的深度学习方法. 它有效地结合了来自非结构化的摄像机组的图像,比传统方法提高了速度和准确性.

关键词:
图像混合的混合图像.图像拼接 图像拼接 图像拼接场景表现场景表示自主监督学习学习没有结构的摄像机阵列.

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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 图像处理 图像处理

背景情况:

  • 传统的图像拼接方法与非结构化的摄像机阵列和场景重叠作斗争.
  • 顺序对接可以导致级联错误和降低效率.

研究的目的:

  • 介绍一种基于深度学习的方法,用于从大型,非结构化的摄像机组中拼接图像.
  • 为了提高复杂场景的图像拼接的效率和准确性.

主要方法:

  • 使用SandFall算法将多个摄像头的数据同时转换成一个缩小的固定数组.
  • 使用定制的卷积神经网络来处理转换的数据.
  • 实施无监督培训方法,使用生成对抗网络指标和监督学习.

主要成果:

  • 拟议的方法同时拼接图像,避免了顺序方法的级联错误.
  • 在CPU和GPU上实现了大约1/7的传统方法的处理时间.
  • 交付的结果与已建立的图像拼接技术一致.

结论:

  • 深度学习方法为图像拼接的速度和效率提供了显著的改进.
  • 这种方法有效地处理复杂的场景和非结构化的摄像头设置.
  • 同步处理和新的训练方法提高了图像拼接的稳定性和性能.