Earticle

초록

영어

In order to improve the recognition accuracy of vision system on tomato picking robots, the paper proposed a method of feature extraction and recognition for ripe tomato based on illumination irrelevant images and support vector machine (SVM). In this method, we adopted vector median filter (VMF) to process the tomato images to eliminate noise and make the images more smooth firstly. To avoid the effects of natural environment illumination to the vision system, we processed tomato images and obtained the tomato illumination irrelevant images according to color constancy algorithm of the single pixel. Secondly, we segmented illumination irrelevant images using OSTU method, separated multiple objects by a watershed algorithm based on distance transform and got the target area with mathematical morphology. Also we extracted color, shape and textural features of the ripe tomatoes. Finally, we did experiments on recognizing tomatoes using support vector machine (SVM) with different kernel functions. At the same time, in order to obtain optimal model of SVM, we adopted cross validation and grid search method to optimize the model parameters. The experiment results show that illumination irrelevant processing not only can eliminate the influence of light intensity, but save a gray transferring step for further image segmentation. SVM with radial basis function is better than other kernel functions SVM and the tomato recognition accuracy is 95.7%. Through optimizing the parameter C and r of radial basis function, the tomato recognition accuracy reaches up to 96.9% with the increase of 1.2% when C and r is 4 and 16 respectively. This proves that it's feasible and effective to optimize SVM's parameters by cross validation and grid search method, which provide foundation for further research on vision system of tomato picking robots.

목차

Abstract
 1. Introduction
 2. Illumination Irrelevant Processing for Tomato Images
 3. Image Segmentation and Multi-Object Extraction
 4. Feature Extraction of Tomato Images
  4.1. Color Feature Extraction
  4.2. Shape Feature Extraction
  4.3. Texture Feature Extraction
 5. Experiments on Tomato Recognition and Optimization Based onSVM
  5.1. SVM and Kernel Functions
  5.2. Recognition Experiments and Analysis
  5.3. Parameter Optimization
 6. Conclusions
 Acknowledgments
 References

키워드

저자

• Xifeng Liang [ College of Mechanical and Electrical Engineering, China Jiliang University, China ]
• Zhengshuai Jiang [ College of Mechanical and Electrical Engineering, China Jiliang University, China ]
• Binrui Wang [ College of Mechanical and Electrical Engineering, China Jiliang University, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605