• DocumentCode
    811219
  • Title

    Tracking of tubular molecules for scientific applications

  • Author

    Parvin, B.A. ; Peng, C. ; Johnston, W. ; Maestre, F.M.

  • Author_Institution
    Div. of Comput. Sci. & Eng., Lawrence Berkeley Nat.Lab., CA, USA
  • Volume
    17
  • Issue
    8
  • fYear
    1995
  • fDate
    8/1/1995 12:00:00 AM
  • Firstpage
    800
  • Lastpage
    805
  • Abstract
    In this paper, we present a system for detection and tracking of tubular molecules in images. The automatic detection and characterization of the shape, location, and motion of these molecules can enable new laboratory protocols in several scientific disciplines. 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 molecules, the perceptually significant features are antiparallel 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
  • Keywords
    dynamic programming; macromolecules; object detection; physics computing; tracking; antiparallel line segments; automatic detection; bounding polygon; characterization; dynamic programming; geometric properties; object detection; object localization; scientific applications; symmetry axis; time sequence; tubular molecule tracking; Boundary conditions; Feature extraction; Image segmentation; Laboratories; Motion detection; Object detection; Pixel; Protocols; Shape; Solid modeling;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.400570
  • Filename
    400570