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Trajectory segmentation algorithm from medical community.

Helmuth et al 2007

"The algorithm relies on the reduction of the information available by identification of specific features on the virus trajectories. It overcomes many of the limitations of averaging moving-window methods and manual thresholding (Huet et al., 2006), and it enables the detection of short pattern segments and the extraction of physical parameters that describe each pattern."

A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells.

Helmuth et al 2007  Journal of Structural Biology 159 (2007) 347–358. doi:10.1016/j.jsb.2007.04.003

Abstract. Biological trajectories can be characterized by transient patterns that may provide insight into the interactions of the moving object
with its immediate environment. The accurate and automated identification of trajectory motifs is important for the understanding of the
underlying mechanisms. In this work, we develop a novel trajectory segmentation algorithm based on supervised support vector classification.
The algorithm is validated on synthetic data and applied to the identification of trajectory fingerprints of fluorescently tagged
human adenovirus particles in live cells. In virus trajectories on the cell surface, periods of confined motion, slow drift, and fast drift are
efficiently detected. Additionally, directed motion is found for viruses in the cytoplasm. The algorithm enables the linking of microscopic
observations to molecular phenomena that are critical in many biological processes, including infectious pathogen entry and signal