The Agriculture Vision Intelligent Image Segmentation Algorithm Based on Machine Learning
Minghui Deng, Shaopeng Zhu, Ming Li
Available Online April 2015.
- https://doi.org/10.2991/icmra-15.2015.131How to use a DOI?
- Image segmentation; machine vision; Random Forest
- Image segmentation and feature extraction are the premise for machine vision system to analyze and identify the image. Threshold image segmentation algorithm according to the method of two dimension threshold has a lot of calculation in calculating the threshold, and the minimum error threshold method can not use the spatial information of image. This paper presents an intelligent image segmentation algorithm with Random Forest theory based on the night segmentation and feature extraction technology. The Random Forest algorithm shows unique advantages in dealing with small sample size, high-dimensional feature space, and complex data structures. An algorithm of vision image segmentation and feature extraction based on Random Forest is designed. Experimental results show that the segmentation process of this algorithm has less control parameters and faster convergence speed.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Minghui Deng AU - Shaopeng Zhu AU - Ming Li PY - 2015/04 DA - 2015/04 TI - The Agriculture Vision Intelligent Image Segmentation Algorithm Based on Machine Learning BT - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation PB - Atlantis Press SP - 676 EP - 680 SN - 2352-538X UR - https://doi.org/10.2991/icmra-15.2015.131 DO - https://doi.org/10.2991/icmra-15.2015.131 ID - Deng2015/04 ER -