The Agriculture Vision Image Segmentation Algorithm Based on Improved Quantum-Behaved Particle Swarm Optimization

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 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.

Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera

“…Here we provide some experiments to verify the superiority of our algorithm, comparing with several state-of-the-art approaches utilizing quantum-behaved particle swarm optimization (QPSO) for muti-threshold searching [20], fuzzy c-means clustering (FCM) [16], pulse coupled neural network-based segmentation (PCNN) [14], Co-segmentation [53] and the traditional LDA, seen from Figures 17-20. We use red, green, and blue color to represent the labeled area of fruits, leaves, and the background, respectively.…”

“…We selected three unsupervised segmentation algorithms [20], [16], and [14] in the field of plant segmentation for comparative experiments. Through the results, we can see that when the same segmentation algorithm is applied to diverse backgrounds, their weaknesses will emerge at different level.…”

“…Through the results, we can see that when the same segmentation algorithm is applied to diverse backgrounds, their weaknesses will emerge at different level. In [20], quantum-behaved particle swarm optimization (QPSO) is actually a heuristic method for multi-threshold random search optimization. The pixel value is regarded as a particle and its position is constantly updated, combined with the spatial information of images.…”

“…We notice that some statistical approaches surpass many other methods and have been gaining more and more attention. These approaches are often applied by modeling the image to achieve the maximum probability distribution of each pixel, or by solving an optimization problem through minimizing an energy function [20][21][22][23].…”

Threshold image target segmentation technology based on intelligent algorithms

“…Firstly, the PSO (solution set) and the dimension of search space are set according to the problem solved. Then, the individual optimal solution and global optimal solution are searched continuously according to the iteration formula of velocity and location [13]:…”