Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation

The Agriculture Vision Intelligent Image Segmentation Algorithm Based on Machine Learning

Authors
Minghui Deng, Shaopeng Zhu, Ming Li
Corresponding Author
Minghui Deng
Available Online April 2015.
DOI
https://doi.org/10.2991/icmra-15.2015.131How to use a DOI?
Keywords
Image segmentation; machine vision; Random Forest
Abstract
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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Mechatronics, Robotics and Automation
Part of series
Advances in Computer Science Research
Publication Date
April 2015
ISBN
978-94-62520-76-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmra-15.2015.131How to use a DOI?
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  - 3rd International Conference on Mechatronics, Robotics and Automation
PB  - Atlantis Press
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  -