Matching and extracting the target curve from a large dataset are time-consuming and laborious tasks when traditional feature detection and tracking methods based on the temporal curve analysis are used because prior knowledge is required to determine the shape of the temporal curve of the region of interest.In this study

we introduce a novel method based on asymmetric Gaussian function to conduct real-time feature detection and tracking for time-varying volume visual analysis. A low-pass filter is first employed to smoothen the original temporal curve

which helps achieve the minimum data points accurately. The original temporal curve can be further subdivided into several segments according to the minimum points. We then take advantage of asymmetric Gaussian functions to fit the segments and to achieve a new Gaussian temporal curve

which can be further expressed on the basis of several simple Gaussian parameters. A convenient interface in which users can select the features of interest by means of visibility analysis along the viewing rayis also provided. Therefore

the evolution of the selected features can be further tracked in different time steps by matching the corresponding Gaussian parameters. To demonstrate the effectiveness of the proposed method

time-varying simulative data are used to conduct feature detection and interactive visualization. A large number of experimental results indicate that the proposed method can detect time-varying features accurately and quickly. The proposed technique expresses the basic temporal curve with new asymmetric Gaussian temporal curve and then takes the simple Gaussian parameter matching as a substitute for the complicated temporal curve matching process. Compared with traditional methods

the proposed approach can conduct feature detection and tracking in real time.