Automatically finding the number of clusters based on simulated annealing

Based on simulated annealing (SA), automatically finding the number of clusters (AFNC) is proposed in this paper to determine the number of clusters and their initial centers. It is a simple and automatic method that combines local search with two widely-accepted global analysis techniques, namely careful-seeding (CS) and distance-histogram (DH). The procedure for finding a cluster is formulated as mountain-climbing, and the mountain is defined as the convergent domain of SA.When arriving at the peak of one mountain, AFNC has found one of the clusters in the dataset, and its initial center is the peak. Then, AFNC continues to climb up another mountain from a new starting point found by CS till the termination condition is satisfied. In the procedure of climbing-up mountain, the local dense region for searching the next state of SA is found by analyzing the distance histogram. Experimental results show that AFNC can achieve consistent performance for a wide range of datasets.     

基于客户机／服务器结构的房地产管理系统

在分析了房地产楼盘管理与营销特点的基础上，提出了一个基于客户机／服务器结构的房地产管理系统设计方案，并给出了开发环境和应用实例。     

Technique based on image pyramid and bayes rule for noise reduction in unsupervised change detection

In this paper, a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed. By using Gaussian pyramid to process two multitemporal images respectively, two image pyramids are constructed. The difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image pyramids. By resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them, a map being similar to the difference image is obtained. The difference image is generated by point-by-point subtraction between the two multitemporal images directly. At last, the Bayes rule is used to distinguish the changed pixels. Both synthetic and real data sets are used to evaluate the performance of the proposed technique. Experimental results show that the map from the proposed technique is more robust to noise than the difference image.