The Research Of Image Segmentation Method Based On Improved Adaptive Genetic Algorithm And Otsu Method

Image segmentation plays a key role in many practical applications of image engineering, the accuracy of image segmentation is very important to image analysis, image recognition and image coding. It is a very important direction to improve image segmentation by integrating various image segmentation algorithms.In order to improve the effectiveness and efficiency of image segmentation, adaptive genetic algorithm and Otsu method are improved. A method of image segmentation based on improved adaptive genetic algorithm and Otsu method is proposed. The main contents are as follows:(1) The traditional method of image segmentation and image segmentation method based on genetic algorithm is analyzed.(2) To analysis the characteristics of the existing genetic algorithms, then an improved genetic algorithm is proposed. By considering the differences of individuals and the differences of population distribution to adaptively adjust the crossover rate and mutation rate of population. Experiments show that the stability of the algorithm and the accuracy has been greatly improved.(3) In order to improve the Otsu method, an improved Otsu method is proposed by considering the histogram of the target and background. The algorithm to determine the optimal threshold by reducing the threshold range and the minimum variance ratio. The method has the advantage of high segmentation accuracy and fast calculation speed.(4) An improved genetic algorithm based on the Otsu method is proposed by considering the best threshold deviation and the prematurity of genetic algorithm, so that to get the optimal threshold image segmentation. The simulation experiments show that the algorithm has strong robustness and good convergence.Finally, a method of image segmentation based on improved adaptive genetic algorithm and Otsu method is proposed in this paper. The theoretically analysis and simulate experiments show that the thresholds is more accurate and it consumes less time greatly by using this new method, compared with other genetic algorithm based image segmentation.