Tracking of Tubular Objects for Scientific Applications

    Bahram Parvin
    Carrie Peng
    William Johnston
    Marcos Maestre

    Imaging and Distributed Computing Group
    Information and Computing Sciences Division
    Lawrence Berkeley Laboratory
    Berkeley, CA 94720

Publication number: LBL-34718

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In this paper, we present a system for detection, tracking and representation of tubular objects in images. The uniqueness of the proposed system is twofold: at the macro level, the novelty of the system lies in the integration of object localization and tracking using geometric properties; at the micro level, in the use of high and low level constraints to model the detection and tracking subsystem. The underlying philosophy for object detection is to extract perceptually significant features from the pixel level image, and then use these high level cues to refine the precise boundaries. In the case of tubular objects, the perceptually significant features are anti-parallel line segments or, equivalently, their axis of symmetries. The axis of symmetry infers a coarse description of the object in terms of a bounding polygon. The polygon then provides the necessary boundary condition for the refinement process, which is based on dynamic programming. For tracking the object in a time sequence of images, the refined contour is then projected onto each consecutive frame. In addition, the system provides an axis of symmetry representation of object for subsequent scientific analysis.

Click here to see a movie of DNA tracking.