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Tracking of Tubular Molecules for Scientific Applications
August 1995 (vol. 17 no. 8)
pp. 800-805
ASCII Text
x 
 
B.a. Parvin, C. Peng, W. Johnston, F.m. Maestre, "Tracking of Tubular Molecules for Scientific Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 800-805, August, 1995.
 
 
BibTex
x
 
@article{ 10.1109/34.400570,
author = {B.a. Parvin and C. Peng and W. Johnston and F.m. Maestre},
title = {Tracking of Tubular Molecules for Scientific Applications},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {17},
number = {8},
issn = {0162-8828},
year = {1995},
pages = {800-805},
doi = {http://doi.ieeecomputersociety.org/10.1109/34.400570},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Tracking of Tubular Molecules for Scientific Applications
IS - 8
SN - 0162-8828
SP800
EP805
EPD - 800-805
A1 - B.a. Parvin,
A1 - C. Peng,
A1 - W. Johnston,
A1 - F.m. Maestre,
PY - 1995
VL - 17
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 

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.

[1] A.A. Amini,R.W. Curwen,R.T. Constable,, and J.C. Gore,“MR physics-based snake tracking and dense deformations of tagged mr cardiac images,” AAAI Conf. Applications of Computer Vision to Medical Image Processing, pp. 126-130, 1994.
[2] A.A. Amini,T.E. Weymouth,, and R.C. Jain,“Using dynamic programming for solving variational problems in vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 9, pp. 855-867, 1990.
[3] R.A. Brooks,“Model-based three-dimensional interpretations of two-dimensional images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, no. 2, pp. 140-150, 1983.
[4] C. Bustamante,“Direct observation and manipulation of single dna molecules usingfluorescence microscopy,” Ann. Review of Biophysics and Biophysical Chemistry, vol. 20, pp. 415-446, 1991.
[5] C. Bustamante,T. Houseal,D. Beach,, and F.M. Maestre,“Fluorescence microscopy of the dynamics of supercoling, folding, andcondensation of bacterial chromosomes, induced by acridine orange,” J. Biomolecular Structure and Dynamics, vol. 8, pp. 643-655, 1990.
[6] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, June 1986.
[7] Q. Huang and C. Stockman,“Generalized tube model: Recognizing 3D elongated objects from 2Dintensity images,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 104-109,New York, June 1993.
[8] A. Huertas,W. Cole,, and R. Nevatia,“Detecting runways in complex airport scenes,” Computer Vision, Graphics, and Image Processing, vol. 51, no. 2, pp. 107-145, Aug. 1990.
[9] M. Kass,A. Witkin,, and D. Terzopoulos,“Snakes: Active contour models,” Int’l J. Computer Vision, vol. 1, pp. 321-331, 1988.
[10] F. Leymarie and M. Levine, "Tracking deformable objects in the plane using an active contour model, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 6, pp. 617-634, June 1993.
[11] C. Nastar and N. Ayache, "Classification of Nonrigid Motion in 3D Images Using Physics-Based Vibration Analysis," Proc. IEEE Workshop Biomedical Image Analysis,Seattle, June 1994.
[12] C.M. Gorritz and C. Medina, "Engaging Girls with Computers through Software Games," Comm. ACM, Jan. 2000, pp. 42-49.
[13] A. Pentland and B. Horowitz, “Recovery of Non-Rigid Motion and Structure,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 7, pp. 730-742, July 1991.
[14] A. Pentland and S. Sclaroff, "Closed-Form Solutions for Physically-Based Shape Modeling and Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 7, pp. 715-729, July 1991.
[15] R.E. Bellman Dynamic Programming. Princeton, N.J.: Princeton Univ. Press, 1957.
[16] L. Song and M. Maestre,“Unhooking dynamics of u-shaped dna molecule undergoing gel electrophoresis,” J. Biomolecular Structure and Dynamics, vol. 9, pp. 87-99, 1991.
[17] D.J. Williams and M. Shah,“A fast algorithm for active contours and curvature estimation,” Computer Vision, Graphics, Image Processing, vol. 55, pp. 14-26, 1992.

Citation:
B.a. Parvin, C. Peng, W. Johnston, F.m. Maestre, "Tracking of Tubular Molecules for Scientific Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 800-805, Aug. 1995, doi:10.1109/34.400570
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