计算机与现代化

• 图像处理 • 上一篇    下一篇

基于随机Gabor特征的半参考农作物图像质量评价方法

  

  1. (贵州航天智慧农业有限公司,贵州贵阳550081)
  • 收稿日期:2019-09-15 出版日期:2020-05-20 发布日期:2020-05-21
  • 作者简介:吴世海(1979-),男,贵州三穗人,高级工程师,本科,研究方向:农业信息化,E-mail: wsh3247@163.com; 鲍义东(1982-),男,浙江兰溪人,高级工程师,博士,研究方向:人工智能,E-mail: baoyidong2012@163.com。
  • 基金资助:
    贵州省科技厅重大专项项目([2016]3001)

Reduced-reference Crop Image Quality Assessment Based on Random Gabor Feature

  1. (Guizhou Aerospace Intelligent Agriculture Co., Ltd., Guiyang 550081, China)
  • Received:2019-09-15 Online:2020-05-20 Published:2020-05-21

摘要: 伴随着农业现代化、信息化、自动化的深化改革,信息技术在其发展的进程中发挥着举足轻重的作用,并逐渐服务于农业生产的各个方面。其中,基于计算机视觉和数字图像处理技术的新型智慧农业系统已经成为农业信息化发展的研究重点和热点。为此,本文以农作物图像为研究对象,提出一种基于随机Gabor特征的半参考农作物图像质量评价方法。Gabor滤波器的核函数能够较好地描述简单视觉神经元的感受野特性,其多频率和方向特征与人眼视觉系统感知图像的方式类似。基于这个事实,采用Gabor滤波分析农作物图像的纹理和边缘分布特征,建立半参考质量评价模型。实验结果表明,本文提出的农作物图像质量评价方法能够很好地识别和感知农作物图像的质量退化,在新型智慧农业系统中发挥着重要作用。

关键词: 智慧农业, 计算机视觉, Gabor特征, 半参考质量评价

Abstract: With the deepening reform of agricultural modernization, informatization and automation, information technology plays an important role in its development process, and gradually serves all aspects of agricultural production. Among them, the new intelligent agricultural system based on computer vision and digital image processing technology has become the research focus and hotspot in the agricultural informatization development. For this reason, this paper takes crop image as the research object, and proposes a reduced-reference crop image quality assessment model based on random Gabor feature. The kernel function of Gabor filter can better describe the field characteristics of simple visual neurons. Its multi-frequency and direction characteristics are similar to the way of humans visual system which perceives images. Based on this fact, Gabor filter is used to analyze the texture and edge distribution characteristics of crop images, and a reduced-reference quality assessment model is established. The experimental results show that the crop image quality assessment model proposed in this paper can recognize and perceive the degradation of crop image quality very well, and plays an important role in the new intelligent agricultural system.

Key words: intelligent agriculture, computer vision, Gabor feature, reduced-reference quality assessment

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