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Membership determination is the key-important step to study open clusters, which can directly influence on the estimation of open clusters’ physical parameters. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm in data mining techniques. In this paper the DBSCAN algorithm has been used for the first time to make the membership determination of the open clusters NGC 6791 and M 67 (NGC 2682). Our results indicate that the DBSCAN algorithm can effectively eliminate the contamination of field stars. The obtained member stars of NGC 6791 exhibit clearly a doubled main-sequence structure in the color-magnitude diagram, implying that NGC 6791 may have a more complicated history of star formation and evolution. The clustering analysis of M67 indicates the presence of mass segregation, and the distinct relative motion between the central part and the outer part of the cluster. These results demonstrate that the DBSCAN algorithm is an effective method of membership determination, and that it has some advantages superior to the conventional kinematic method.

Three-D rounding photography system is an application of Desktop Virtual Reality. It takes the pictures around the objects with the computer, and then deasl with them for the 360 degree display in the local computer or a web page. The Three-D visual sense and image real-time amplification of this system are the key technical problems. This paper researches on this system and proposes a new solution based on the Silverlight technology togher withan improved triangle internal interpolation method based on the barycentric coordinates properties for the image real-time amplification.The experimental results show that the solution meets the visual sense and interaction Requirements of the Three-D Rounding Photography system. And the improved algorithm reduces the operation time efficiently, thus it has some practical value.

Due to the variety and the complexity of marrow cells image, better results may be achieved by adopting multi-threshold for the image segmentation. However, with regard to no significant peak and valley values in the histogram, it is too difficult to determine thresholds. Therefore, a new method of combining the spatial scales filter and the wavelet transform analysis is adopted to find suitable thresholds and the obtained edge is optimized by the active contour model. Proposed a new genetic clustering algorithm based on entropy to classify the marrow cells. Through the experiences, better result can be attained.

Aiming at the characteristics of the marrow cell image, proposed a new method to check and measure local regularity and got a appropriate threshold using multi-fractal spectrum meanwhile accurate edges of different kinds of cells can be detected. Using Principal Component Analysis based neural network (GHA) to acquire three principal components and using Simulated Annealing (SA) and BP network to classify and recognize the marrow cells.