Method Of Image Segmentation Based On Swarm Intelligence Threshold

Image segmentation not only is the basic and key task in image processing and computer vision, but also is the foundation of image understanding and pattern recognition. Therefore, fast and efficient segmentation methods have always been one of the most important focuses in image engineering domestic and abroad.Focusing on some swarm intelligence based algorithms presented after2000like Bacterial Foraging (BF) algorithm, this dissertation researches on the new technologies and methods on image segmentation. The main research fruits are summarized as follows.A method based on histogram preprocessing and BF algorithm for noisy image single threshold segmentation is proposed. In this method, discrete wavelet transform is used to suppress the noise in the image firstly. Secondly, the histogram feature of the denoised image is analyzed to shrink the distribution range of the optimal threshold. Then, two-dimensional Otsu is selected as the segmentation objective function, and bacterial foraging algorithm is employed to find the optimal threshold in parallel. Experimental results show that this method performs better than some other methods based on swarm intelligence like genetic algorithm, artificial fish swarm algorithm as far as convergence speed, stability and segmentation effect are concerned.(2) After the principle and feature of basic Artificial Bee Colony algorithm(ABC) are deeply analyzed, a method of SAR image in spatial single threshold segmentation based on fuzzy ABC algorithm is proposed. In this method, grey morphology operations are employed to reduce the inherent image noise, and the searching range is reduced on the basis of the histogram feature of the denoised image. Simultaneously, a fuzzy function is introduced to refine the motion of bees, and fast search the optimal threshold. Experimental results show that the proposed method not only is robust to the speckle noise in SAR images, but also is superior to the segmentation.methods based on Genetic algorithm or Artificial Fish Swarm algorithm in terms of segmenting speed and quality.(3) Aim at the problem of complex computing in searching higher dimensions threshold by exhaustive search algorithm, an image multi-threshold segmentation method based on Cuckoo Search(CS) algorithm is proposed, the method designs the fitness of intelligent individuals by Otsu, and uses the favorable parallel searching performance of cuckoo search algorithm to fast and accurately find the optimal threshold of the image to be segmented. Experiment results show that compared with bacterial foraging algorithm and artificial bee colony algorithm, the performance of cuckoo search algorithm is optimal in terms of its fast speed and better quality threshold.