›› 2014, Vol. 26 ›› Issue (1): 200-205.

• 生物系统工程 • 上一篇    

基于椭圆傅里叶描述子的香蕉形状识别

胡孟晗1,董庆利1,*,刘宝林1,张重阳2,叶飞2
  

  1. 1上海理工大学 医疗器械与食品学院,上海 200093; 2上海交通大学 图像通信与信息处理研究所,上海 200240
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2014-01-25 发布日期:2014-07-09

Banana shape recognition based on elliptic Fourier descriptor

HU Meng-han;DONG Qing-li;*;LIU Bao-lin;ZHANG Chong-yang;YE Fei   

  1. 1School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2 Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2014-01-25 Published:2014-07-09

摘要: 为实现香蕉形状的识别,文章提出了一种基于计算机视觉和椭圆傅里叶描述子的形状识别方法。先由专家挑选果指果形为弯、微弯、末端直和直的香蕉共107枚,再获取预处理后图像的边界链码并提取其椭圆傅里叶系数,通过比较原形状和重建形状来选取合适的系数个数,然后用主成分分析对非负系数进行降维,最后进行模糊C均值聚类。研究结果表明,可以将该机器识别方法应用于香蕉形状的识别,为实现香蕉及其深加工产品的增值提供了一种技术手段,并为进一步实现香蕉果把和果穗的形状识别奠定了基础。

关键词: 椭圆傅里叶描述子, 图像处理, 形状识别, 水果, 计算机视觉

Abstract: In this paper, a method based on computer vision and elliptic Fourier descriptor (EFD) was developed to recognize the banana shape. The number of banana with shapes of the curved, slightly curved, end-straight and straight was 107, and the bananas were previously indentified by the experts. Subsequently, the boundary chain codes of the pre-processed images of these bananas were obtained for extracting the elliptic Fourier coefficients. The suitable number of coefficients was determined by comparing the original and reconstructed banana shape, and then the principal component analysis was used to reduce the number of non-negative coefficients. Finally, the classification was achieved by the fuzzy C-means clustering. The results demonstrated that this machine method could be applied for banana shape identification in practice. This study provided a technological means for the value increase of banana and its secondary products, and also might be the basis of realizing the shape recognition of banana hand and bunch in the future.

Key words: elliptic Fourier descriptor, image processing, shape recognition, fruit, computer vision