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The new type of intelligent optimization algorithm and its application in image segmentation

Author: LiangJianHui
Tutor: MaMiao
School: Shaanxi Normal University
Course: Applied Computer Technology
Keywords: Image Segmentation Swarm Intelligence Artificial Fish School Algorithm Bacterial foraging algorithm Artificial Bee Colony Algorithm
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 157
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Abstract


In recent years, with the rise of artificial intelligence and artificial life, many new swarm intelligence optimization algorithm typical artificial fish swarm algorithm and bacterial foraging algorithm and artificial bee colony algorithm, these algorithms produce a relatively late time , very few applications in image processing. Image segmentation is a critical step in image processing to image analysis, its quality will directly impact the effect of the subsequent image analysis and image understanding, fast, and efficient segmentation method has been the concern of researchers hotspot. 2002 a new generation of swarm intelligence optimization algorithm, focusing on analysis of its principles, characteristics, and try to apply them to image segmentation technology, the major innovative achievements are reflected in: (1) In-depth analysis of artificial fish school \clusters \On this basis, put forward an improved algorithm based on artificial fish school SAR image thresholding methods, the method first treat split image three stationary wavelet transform preprocessing, and then, using a two-dimensional reconstructed image and its mean image The trace of the dispersion matrix the histogram to structure class fitness function as AFSA. Finally, the use of a fast optimization improved artificial fish swarm algorithm to find the optimal threshold. The experimental results show that, compared with the traditional method of image segmentation algorithm based on artificial fish, and significantly improve the segmentation quality and segmentation speed. (2) in-depth analysis of the behavior patterns of bacterial foraging algorithm flora \On this basis, the SAR image based on improved bacterial foraging algorithm threshold segmentation method. The method uses the improved two-dimensional gray entropy model bacterial foraging algorithm fitness function, by flora chemokines, copy and disperse the the three behavioral mode parallel search optimal threshold. The preliminary experiment, the convergence speed, stability and segmentation, superior image segmentation method based on genetic algorithms and artificial fish swarm algorithm. (3)-depth study of mining bees \Fast SAR Image Segmentation method of working bee colony algorithm. Firstly secondary noise suppressing reconstructed to obtain an overview of the image after the low-frequency coefficients of the wavelet domain to obtain filtered image reconstructed to obtain the gradient image, and then the high-frequency information of the wavelet domain, and then based on these two images, tectonic filtering - gradient co-occurrence matrix as the fitness function of the artificial bee colony algorithm, then, by constructing a gray entropy model. Finally, the use of bees swarm intelligence to quickly determine the best threshold. The experimental results show that this method is superior to parallel segmentation method based on genetic algorithms, artificial fish swarm algorithm. (4) The proposed method of image segmentation based on the grayscale morphological and artificial bee colony algorithm, the method first treat split image to do grayscale morphological pretreatment to suppress image noise, then the use of 2D Otsu method designed fitness function of the worker bee colony algorithm; Finally, the parallel artificial bee colony algorithm optimization ability is fast approaching the optimal threshold. The experimental results show that the method in the segmentation of infrared images and SAR images, separated from the target is more precise, more suitable for subsequent image analysis and processing.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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