Efficient segmentation of spatio-temporal data from simulations

Detecting and tracking objects in spatio-temporal datasets is an active research area with applications in many domains. A common approach is to segment the 2D frames in order to separate the objects of interest from the background, then estimate the motion of the objects and track them over time. Most existing algorithms assume that the objects to be tracked are rigid. In many scientific simulations, however, the objects of interest evolve over time and thus pose additional challenges for the segmentation and tracking tasks. We investigate efficient segmentation methods in the context of scientific simulation data. Instead of segmenting each frame separately, we propose an incremental approach which incorporates the segmentation result from the previous time frame when segmenting the data at the current time frame. We start with the simple K-means method, then we study more complicated segmentation techniques based on Maxkov random fields. We compare the incremental methods to the corresponding sequential ones both in terms of the quality of the results, as well as computational complexity.