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OSC-CO2:用于具有多个特征的植物状态变化的共同注意力和共同细分框架

Rubi Quiñones^1,2, Ashok Samal¹, Sruti Das Choudhury^1,3

¹School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States.

Frontiers in plant science

|November 29, 2023

概括

使用共同注意力-共同细分 (OSC-CO^2) 的对象状态变化提高了植物图像细分的准确性. 这种深度学习框架增强了对象检测,以在不同的成像模式和视图中对不断变化的植物形态进行检测.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
植物科学植物科学

背景情况:

传统的共细分和共同注意力方法与对象的细分斗争,在不同的成像模式和视图中表现出形态变化,特别是植物.
精确的植物图像细分对于高通量表型化和了解植物生长动态至关重要.

研究的目的:

引入物体状态变化,使用协同注意-协同细分 (OSC-CO^2) 框架来准确细分具有进化的形态的植物.
为了解决处理多模式,多视图植物成像数据的现有方法的局限性.
提出一个新的数据集,CosegPP+,用于评估植物生长序列的细分性能.

主要方法:

开发了一种端到端无监督的深度学习框架 (OSC-CO^2),该框架集成了基于共同注意的卷积神经网络 (CNN) 和基于共分化的密集条件随机场 (CRF).
该框架处理,分析,选择和结合细分结果以生成最终的细分图像.
利用红外,可见光和光摄像头捕捉的植物生长序列在遥感,高通量表型平台上.

主要成果:

与最先进的细分和共细分方法相比,OSC-CO^2表现出更高的性能.
细分精度提高了3%至45%的范围.
CosegPP+数据集提供了对该框架有效性的定量验证.

结论:

OSC-CO^2框架有效地解决了对象与变化状态的细分的挑战,特别是在复杂的植物图像中.
这种方法显著提高了高维,多模式和多视图植物成像中的细分精度.
开发的框架和数据集有助于推进自动化植物表型和分析.

关键词:

协同细分是指共细分.高通量植物表型化图像分析图像分析图像序列的图像序列的时间.多个维度的多个维度.多个功能多个功能多个功能.对象状态变化对象状态变化细分化细分化的细分化

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