Application of SVM Algorithm for Particle Swarm Optimization in Apple Image Segmentation

DOI

10.2991/iccse-15.2015.3How to use a DOI?

Keywords

image segmentation; machine vision; particle swarm algorithm; parameter optimization; SVM

Abstract

The apple image segmentation is the key technology of identification and location in the apple-picking machine vision system. On account of huge errors in the process of discriminating fruits by apple-picking robots at present and the long-time processing, the SVM theory in fingerprint image segmentation method is conducted. Combined with the global search ability of particle swarm optimization in solving combinational optimization problems, the SVM partitioning algorithm, which is based on the parameter optimization of particle swarm, is put forward. The results show that this algorithm makes the separation of apple fruits and the image background come true. It also preserves the outline of apples, then polishes the image after segmentation by the close operation in mathematical morphology, which eliminates the pore phenomenon effectively and provides convenience for the further apple-picking and apple-discriminating.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qirui Huang
PY  - 2015/07
DA  - 2015/07
TI  - Application of SVM Algorithm for Particle Swarm Optimization in Apple Image Segmentation
BT  - Proceedings of the 2015 International Conference on Computational Science and Engineering
PB  - Atlantis Press
SP  - 12
EP  - 16
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccse-15.2015.3
DO  - 10.2991/iccse-15.2015.3
ID  - Huang2015/07
ER  -