The Agriculture Vision Image Segmentation Algorithm Based on Improved Quantum-Behaved Particle Swarm Optimization
| Autor: | Shao Peng Zhu, Ming Hui Deng, Zhan Cheng Li |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Segmentation-based object categorization
Machine vision business.industry Feature extraction Scale-space segmentation Particle swarm optimization General Medicine Image segmentation Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence Multi-swarm optimization business Algorithm Mathematics Feature detection (computer vision) |
| Zdroj: | Applied Mechanics and Materials. :1947-1950 |
| ISSN: | 1662-7482 |
| DOI: | 10.4028/www.scientific.net/amm.713-715.1947 |
| Popis: | 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 improved quantum-behaved particle swarm optimization based on the night segmentation and feature extraction technology. This paper introduces the QPSO algorithm based on multi group and multi stage improvement. The QPSO optimizing algorithm gradually approaches the global optimum threshold value to achieve better convergence and stability. An algorithm of vision image segmentation and feature extraction based on improved quantum-behaved particle swarm optimization is designed. Experimental results show that the optimization process of this algorithm has less control parameters and faster convergence speed. |
| Databáze: | OpenAIRE |
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