Research On Image Segmentation, Evaluation And Image Three-Dimensional Surface Reconstruction

Image segmentation and image three-dimensional(3-D) surface reconstruction are two important research fields in image processing. There are limitations about existing image segmentation when applying for the best threshold value. There are problems of overlap, adhesion in image segmentation, and also there are some difficulties in evaluation of the segmentation quality. It is often unable to find the optimal solution when using multi-objective optimization method to solve 3-D surface reconstruction of the images. To solve these problems, based on fuzzy set theory, this thesis discuss the optimal solution to image segmentation by parallel searching methods, evaluating the quality of images segmentation method by means of particle parameters, reconstructing the 3-D surface using the circle maximum-minimum solution for image. The whole thesis includes follow parts:In image segmentation, the segmentation algorithms based on threshold and edge detection were discussed in detail. The ideas of the two algorithms were analyzed. The results of the simulation by different images were analyzed, studied, compared. It can improve the accuracy of edge detection by use the estimate pattern of 22.5°angle in 16 direction and set a reasonable parameters, avoid pseudo-edge by improve the operator of Laplace. The improved operator of Laplace not only detected on edge clearer, but also detected more edges which have not been detected before, avoiding the extraction of some pseudo-edge.Based on the clustering image segmentation, algorithms of hard c-means clustering(HCM) and fuzzy c-means clustering(FCM) were compared by means of the FCM clustering algorithm for image segmentation experiments carried out. However, the initialization was needed in FCM algorithm and there were lots of local minimum in the objective function, if the initialization abstained the local minimum vicinity point, it would cause a convergence to local minimum. In order to solve this problem, global optimization search(GOS) algorithm is proposed in this thesis, using the parallel search algorithm GOS ability of the FCM algorithm to improve the capacity of the formation with a parallel algorithm for the GOS. At the same time, the fact that GOS algorithm are more effective than the traditional FCM clustering algorithm is analyzed , the algorithm performance has been analyzed theoretically, and has been verified through simulation experiment. The evaluation of the quality of image segmentation is one of the most difficult problems to be solved. It is proposed in this thesis that it is possible to calculate the parameters of particle shape and particle size distribution and so on by means of particle image measuring method. So that we can evaluate the accuracy of image segmentation, and compare the seven types of segmentation evaluation methods. It has been proved that this method can better evaluate the advantages and disadvantages of image segmentation algorithms through simulation of the qualitative and quantitative analysis.In images 3-D reconstruction, the multi-objective optimization of fuzzy images mathematical model of 3-D reconstruction to optimize the image has been proposed, and the ordinary objective function of 3-D reconstruction is represented in the form of membership functions, and has been described by new type of mathematical model, and so the circle maximum-minimum solution were proposed. The proposed algorithm can not only predigests the seeking process, but also finds the available solution of original problem automatically.The sequence image of damaged skull and rabbit's thigh were reconstructed in the image 3-D reconstruction. The background material detection, edge detection and reconstruction a damaged skull were studied in this thesis. The simulation experiments verified the feasibility of the algorithm by comparing different algorithms. In order to observe a variety of rabbit's thigh, the contrast media was injected into the rabbit's blood vessel. The feasibility of reconstruction algorithm and the reasonableness of results were discussed through the simulation.A system of experimental platform has been developed based on the above research, which is based on image segmentation and image 3-D reconstruction. This system can provide image processing and analysis, especially the GOS algorithm, image reconstruction algorithms and measurement methods of particle's parameters and so on, at the same time the application of the system of experimental platform is forecasted in this thesis.The thesis ends with the conclusions of the achieved and future research, which extend our thoughts in the future.